System

Abstract

An object of a system according to an embodiment is to efficiently perform space mission planning, support for astronauts, space education, and space data analysis.SOLUTION: A system includes a collection part, a proposal part, a monitoring part, a support part, a generation part, a provision part, an analysis part, and a summarization part. The collection unit collects past mission data and a current technical status. The proposal section analyzes the data collected by the collection section and proposes an efficient mission plan. The monitoring unit monitors a health condition and a work situation of the astronaut. The support unit provides necessary support based on the data obtained by the monitoring unit. The generation unit generates educational content related to space. The provision unit provides the content generated by the generation unit. The analysis unit analyzes a large amount of space data. The summarization unit summarizes and provides important information obtained by the analysis unit.SELECTED DRAWING: Figure 1

Description

[Technical Field]

[0001] The technology of the present disclosure relates to a system. [Background technology]

[0002] Patent document 1 discloses a persona chatbot control method performed by at least one processor, the method including the steps of receiving a user utterance, adding the user utterance to a prompt including an instruction sentence related to a description of the chatbot character, encoding the prompt, and inputting the encoded prompt into a language model to generate a chatbot utterance in response to the user utterance. [Prior art documents] [Patent documents]

[0003] [Patent Document 1] Japanese Patent Publication No. 2022-180282 Summary of the Invention [Problem to be solved by the invention]

[0004] Conventional technologies are inefficient in space mission planning, astronaut support, space education, and space data analysis, and there is room for improvement.

[0005] The system according to the embodiment aims to efficiently plan space missions, support astronauts, provide space education, and analyze space data. [Means for solving the problem]

[0006] The system according to the embodiment includes a collection unit, a proposal unit, a monitoring unit, a support unit, a generation unit, a provision unit, an analysis unit, and a summarization unit. The collection unit collects past mission data and current technical status. The proposal unit analyzes the data collected by the collection unit and proposes efficient mission plans. The monitoring unit monitors the health and work status of astronauts. The support unit provides necessary support based on the data obtained by the monitoring unit. The generation unit generates educational content related to space. The provision unit provides the content generated by the generation unit. The analysis unit analyzes large amounts of space data. The summarization unit summarizes and provides important information obtained by the analysis unit. [Effects of the Invention]

[0007] The system according to the embodiment can efficiently plan space missions, support astronauts, provide space education, and analyze space data. [Brief explanation of the drawings]

[0008] [Figure 1] 1 is a conceptual diagram showing an example of the configuration of a data processing system according to a first embodiment. [Figure 2] 1 is a conceptual diagram showing an example of main functions of a data processing device and a smart device according to a first embodiment. [Figure 3] FIG. 10 is a conceptual diagram showing an example of the configuration of a data processing system according to a second embodiment. [Figure 4] FIG. 10 is a conceptual diagram showing an example of main functions of a data processing device and smart glasses according to a second embodiment. [Figure 5] FIG. 10 is a conceptual diagram showing an example of the configuration of a data processing system according to a third embodiment. [Figure 6] FIG. 11 is a conceptual diagram showing an example of main functions of a data processing device and a headset-type terminal according to a third embodiment. [Figure 7] FIG. 10 is a conceptual diagram showing an example of the configuration of a data processing system according to a fourth embodiment. [Figure 8]FIG. 10 is a conceptual diagram showing an example of main functions of a data processing device and a robot according to a fourth embodiment. [Figure 9] 1 shows an emotion map onto which multiple emotions are mapped. [Figure 10] 1 shows an emotion map onto which multiple emotions are mapped. DETAILED DESCRIPTION OF THE INVENTION

[0009] An example of an embodiment of a system according to the technology of the present disclosure will be described below with reference to the accompanying drawings.

[0010] First, the terms used in the following description will be explained.

[0011] In the following embodiments, a coded processor (hereinafter simply referred to as a "processor") may be a single arithmetic device or a combination of multiple arithmetic devices. Furthermore, the processor may be a single type of arithmetic device or a combination of multiple types of arithmetic devices. Examples of arithmetic devices include a CPU (Central Processing Unit), a GPU (Graphics Processing Unit), a GPGPU (General-Purpose computing on Graphics Processing Units), an APU (Accelerated Processing Unit), or a TPU (Tensor Processing Unit).

[0012] In the following embodiments, a coded RAM (Random Access Memory) is a memory in which information is temporarily stored and is used as a working memory by a processor.

[0013] In the following embodiments, the coded storage is one or more non-volatile storage devices that store various programs, various parameters, etc. Examples of non-volatile storage devices include flash memory (SSD (Solid State Drive)), magnetic disks (e.g., hard disks), and magnetic tapes.

[0014] In the following embodiments, a communication I / F (Interface) with a symbol is an interface including a communication processor, an antenna, etc. The communication I / F controls communication between multiple computers. Examples of communication standards applied to the communication I / F include wireless communication standards including 5G (5th Generation Mobile Communication System), Wi-Fi (registered trademark), and Bluetooth (registered trademark).

[0015] In the following embodiments, "A and / or B" is synonymous with "at least one of A and B." In other words, "A and / or B" means that it may be only A, only B, or a combination of A and B. Furthermore, in this specification, the same concept as "A and / or B" is also applied when three or more things are expressed connected by "and / or."

[0016] [First embodiment] FIG. 1 shows an example of the configuration of a data processing system 10 according to the first embodiment.

[0017] 1, a data processing system 10 includes a data processing device 12 and a smart device 14. An example of the data processing device 12 is a server.

[0018] The data processing device 12 includes a computer 22, a database 24, and a communication I / F 26. The computer 22 includes a processor 28, a RAM 30, and a storage 32. The processor 28, RAM 30, and storage 32 are connected to a bus 34. The database 24 and the communication I / F 26 are also connected to the bus 34. The communication I / F 26 is connected to a network 54. Examples of the network 54 include a WAN (Wide Area Network) and / or a LAN (Local Area Network).

[0019] The smart device 14 includes a computer 36, a reception device 38, an output device 40, a camera 42, and a communication I / F 44. The computer 36 includes a processor 46, a RAM 48, and a storage 50. The processor 46, the RAM 48, and the storage 50 are connected to a bus 52. The reception device 38, the output device 40, and the camera 42 are also connected to the bus 52.

[0020] The reception device 38 includes a touch panel 38A and a microphone 38B, and receives user input. The touch panel 38A detects contact with a pointer (for example, a pen or a finger) to receive user input by the touch of the pointer. The microphone 38B detects the user's voice to receive user input by voice. The control unit 46A transmits data indicating the user input received by the touch panel 38A and the microphone 38B to the data processing device 12. In the data processing device 12, the specific processing unit 290 (see FIG. 2) acquires the data indicating the user input.

[0021] Output device 40 includes a display 40A and a speaker 40B, and presents data to a user by outputting the data in a form of expression that the user can perceive (e.g., audio and / or text). Display 40A displays visible information such as text and images in accordance with instructions from processor 46. Speaker 40B outputs audio in accordance with instructions from processor 46. Camera 42 is a compact digital camera equipped with an optical system including a lens, aperture, and shutter, and an imaging element such as a CMOS (Complementary Metal-Oxide-Semiconductor) image sensor or a CCD (Charge Coupled Device) image sensor.

[0022] The communication I / F 44 is connected to a network 54. The communication I / Fs 44 and 26 control the exchange of various information between the processor 46 and the processor 28 via the network 54.

[0023] FIG. 2 shows an example of the main functions of the data processing device 12 and the smart device 14.

[0024] 2, in the data processing device 12, a specific process is performed by the processor 28. A specific processing program 56 is stored in the storage 32. The specific processing program 56 is an example of a "program" according to the technology of the present disclosure. The processor 28 reads the specific processing program 56 from the storage 32 and executes the read specific processing program 56 on the RAM 30. The specific process is realized by the processor 28 operating as a specific processing unit 290 in accordance with the specific processing program 56 executed on the RAM 30.

[0025] The storage 32 stores a data generation model 58 and an emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290. The identification processing unit 290 can estimate a user's emotion using the emotion identification model 59 and perform identification processing using the user's emotion. The emotion estimation function (emotion identification function) using the emotion identification model 59 performs various estimations and predictions regarding the user's emotion, including estimation and prediction of the user's emotion, but is not limited to these examples. Furthermore, the estimation and prediction of emotion also includes, for example, emotion analysis.

[0026] In the smart device 14, the specific processing is performed by the processor 46. The storage 50 stores a specific processing program 60. The specific processing program 60 is used together with the specific processing program 56 by the data processing system 10. The processor 46 reads the specific processing program 60 from the storage 50 and executes the read specific processing program 60 on the RAM 48. The specific processing is realized by the processor 46 operating as the control unit 46A in accordance with the specific processing program 60 executed on the RAM 48. Note that the smart device 14 has a data generation model and an emotion identification model similar to the data generation model 58 and the emotion identification model 59, and can also perform processing similar to that of the specific processing unit 290 using these models.

[0027] Note that a device other than the data processing device 12 may have the data generation model 58. For example, a server device (e.g., a generation server) may have the data generation model 58. In this case, the data processing device 12 obtains a processing result (prediction result, etc.) using the data generation model 58 by communicating with the server device having the data generation model 58. Furthermore, the data processing device 12 may be a server device, or may be a terminal device owned by a user (e.g., a mobile phone, a robot, a home appliance, etc.). Next, an example of processing by the data processing system 10 according to the first embodiment will be described.

[0028] (Example 1) A system according to an embodiment of the present invention utilizes generative AI to support space projects. This system analyzes past mission data and current technological status to propose optimal mission plans. For example, when planning an exploration mission to a specific planet, the generative AI considers past exploration data and current technological levels to propose the optimal exploration route and necessary equipment. It also monitors astronauts' health and work status in real time and provides necessary support. For example, when astronauts work long hours, the generative AI monitors their health and provides advice on appropriate rest times and nutritional supplements. Furthermore, it generates educational content about space and provides it to the general public. For example, it generates videos and articles that clearly explain basic space knowledge and the latest research results. Finally, it analyzes massive amounts of space data and summarizes important information. For example, it analyzes astronomical observation data and space exploration results to extract information useful to researchers and the general public. As a result, the use of generative AI is expected to improve the efficiency and development of space projects. For example, it can support the planning of space exploration missions, support astronauts, provide space education and awareness, and analyze and summarize space data.

[0029] A space business support system according to an embodiment includes a collection unit, a proposal unit, a monitoring unit, a support unit, a generation unit, a provision unit, an analysis unit, and a summarization unit. The collection unit collects past mission data and current technical status. For example, the collection unit can collect data such as the success rate of past missions, the technologies used, and environmental conditions. The collection unit can also collect current technical status such as the latest space exploration technology, communication technology, and life support technology. The proposal unit analyzes the data collected by the collection unit and proposes an efficient mission plan. For example, the proposal unit can propose an optimal exploration route and necessary equipment based on past exploration data and the current technical level. The proposal unit can also make proposals to improve mission efficiency using a generation AI. The monitoring unit monitors the health status and work status of astronauts in real time. For example, the monitoring unit can monitor vital data such as astronauts' heart rate, blood pressure, and oxygen saturation. The monitoring unit can also monitor astronauts' work status such as their progress, type of work, and work time. The support unit provides necessary support based on the data obtained by the monitoring unit. For example, the support unit can provide astronauts with advice on appropriate rest times and nutritional supplementation. The support unit can also provide medical and psychological support to maintain the astronauts' health. The generation unit generates educational content related to space. For example, the generation unit can generate educational content such as the history of space, the latest research results, and space exploration technology. The generation unit can also use generation AI to generate educational content that is easy to understand for the general public. The provision unit provides the educational content generated by the generation unit to the general public. For example, the provision unit can provide the generated educational content through a website or application. The provision unit can also provide the educational content in printed form. The analysis unit analyzes large amounts of space data. For example, the analysis unit can analyze astronomical observation data and the results of space exploration. The analysis unit can also use generation AI to analyze the data. The summarization unit summarizes and provides important information obtained by the analysis unit.For example, the summarization unit can extract and provide information useful to researchers and the general public. The summarization unit can also use a generative AI to summarize data. This allows the space business support system according to the embodiment to utilize generative AI to support space businesses, enabling efficient mission planning, support for astronauts, the generation of educational content, and data analysis and summarization.

[0030] The proposal unit can propose efficient exploration routes and necessary equipment based on past exploration data and the current technology level. For example, the proposal unit analyzes past exploration data and proposes the optimal exploration route by taking into account the success rate of the exploration, the technology used, environmental conditions, etc. The proposal unit can also propose an efficient exploration route using the latest exploration technology, communication technology, and life support technology by taking into account the current technology level. Furthermore, the proposal unit can use a generation AI to optimize the exploration route. For example, the proposal unit can input past exploration data and the current technology level into the generation AI and have it output the optimal exploration route. This allows the optimal mission plan to be proposed by taking into account past data and current technology.

[0031] The monitoring unit can monitor the astronaut's vital data and work status in real time. For example, the monitoring unit monitors vital data such as the astronaut's heart rate, blood pressure, and oxygen saturation in real time. The monitoring unit can also monitor the astronaut's work status, such as the progress, type of work, and work time, in real time. Furthermore, the monitoring unit can use the generation AI to detect abnormalities in the vital data and work status. For example, the monitoring unit can input vital data and work status into the generation AI and have it detect abnormalities. This allows the astronaut's health condition and work status to be monitored in real time, allowing appropriate support to be provided.

[0032] The support unit can provide advice on appropriate rest timing and nutritional supplementation. For example, the support unit can suggest appropriate rest timing based on the astronaut's vital data and work status. The support unit can also provide advice on appropriate nutritional supplementation to maintain the astronaut's health. Furthermore, the support unit can use the generation AI to optimize rest timing and nutritional supplementation. For example, the support unit can input vital data and work status into the generation AI and have it output advice on optimal rest timing and nutritional supplementation. This helps maintain the astronaut's health and support efficient work.

[0033] The generation unit can generate educational content based on space-related knowledge and the latest research results. The generation unit generates educational content such as the history of space, the latest research results, and space exploration technology. The generation unit can also use the generation AI to generate educational content that is easy for the general public to understand. For example, the generation unit can input space-related knowledge and the latest research results into the generation AI and have it output educational content. This allows educational content that reflects the latest research results to be generated and provided to the general public.

[0034] The providing unit can provide the generated educational content to the general public. For example, the providing unit can provide the generated educational content through a website or an application. The providing unit can also provide the educational content as printed material. Furthermore, the providing unit can use the generating AI to optimize the method of providing the educational content. For example, the providing unit can input educational content into the generating AI and have it output the optimal method of providing it. In this way, knowledge about space can be spread by providing the generated educational content to the general public.

[0035] The analysis unit can analyze astronomical observation data and the results of space exploration. For example, the analysis unit analyzes astronomical observation data to extract star positions, luminosity, spectral data, etc. The analysis unit can also analyze the results of space exploration to extract probe observation data, geological data, environmental data, etc. Furthermore, the analysis unit can also analyze data using the generation AI. For example, the analysis unit can input observation data and exploration data into the generation AI and have it extract important information. In this way, important information can be extracted by analyzing the astronomical observation data and the results of space exploration.

[0036] The summarization unit can extract and provide information useful to researchers and the general public. For example, the summarization unit summarizes the data obtained by the analysis unit and extracts information useful to researchers and the general public. The summarization unit can also use a generation AI to summarize data. For example, the summarization unit can input analysis data into the generation AI and have it output a summary. This provides useful information to researchers and the general public, deepening their understanding of space data.

[0037] The collection unit can evaluate the reliability of past mission data and prioritize collection of highly reliable data. For example, the collection unit can score the reliability of past mission data and prioritize collection of data with high scores. The collection unit can also filter out low-reliability data and exclude it from collection. Furthermore, the collection unit can use the generation AI to identify highly reliable data sources and prioritize collection of that data. For example, the collection unit can input past mission data into the generation AI and have it evaluate reliability. This improves data accuracy by prioritizing collection of highly reliable data.

[0038] The collection unit can integrate information from different data sources to improve the accuracy of the collection. For example, the collection unit collects the same information from multiple data sources and checks the consistency of the data. The collection unit can also integrate information from different data sources to collect more detailed data. Furthermore, the collection unit can use the generation AI to analyze differences between data sources and improve the accuracy of the collected data. For example, the collection unit can input information from multiple data sources into the generation AI and have it integrate the data. This improves the accuracy of the collected data by integrating information from different data sources.

[0039] The collection unit can select the optimal collection method depending on the type of data to be collected. For example, in the case of text data, the collection unit can efficiently collect the data using an API. In addition, in the case of image data, the collection unit can also collect the data using image recognition technology. Furthermore, in the case of audio data, the collection unit can also collect the data using voice recognition technology. For example, the collection unit can input the type of data to be collected into the generation AI and have it output the optimal collection method. This enables efficient data collection by selecting the optimal collection method depending on the type of data.

[0040] The collection unit can prioritize collection of highly relevant data by taking geographical location information into consideration. For example, the collection unit can prioritize collection of data from a specific region to obtain information specific to the region. The collection unit can also filter highly relevant data based on geographical location information. Furthermore, the collection unit can use the generation AI to set the range of data collection by taking geographical location information into consideration. For example, the collection unit can input geographical location information into the generation AI and have it output highly relevant data. This allows highly relevant data to be collected efficiently by taking geographical location information into consideration.

[0041] The collection unit can analyze social media activities and collect related data. For example, the collection unit analyzes the content of social media posts and collects related data. The collection unit can also analyze social media trends and collect related data. Furthermore, the collection unit can use the generation AI to monitor social media user activities and collect related data. For example, the collection unit can input social media data into the generation AI and cause it to output related data. This allows related data to be collected efficiently by analyzing social media activities.

[0042] The collection unit can customize the collection method by reflecting past feedback. For example, the collection unit can improve data collection methods based on past feedback. The collection unit can also select data to collect by reflecting feedback. Furthermore, the collection unit can use the generation AI to adjust the frequency and timing of collection based on feedback. For example, the collection unit can input past feedback into the generation AI and have it output the optimal collection method. In this way, the collection method can be optimized by reflecting past feedback.

[0043] The suggestion unit can adjust the level of detail of the proposal based on the importance of the mission. For example, the suggestion unit makes a detailed proposal for a mission of high importance. The suggestion unit can also make a concise proposal for a mission of low importance. Furthermore, the suggestion unit can use the generation AI to adjust the level of detail of the proposal based on the importance of the mission. For example, the suggestion unit can input the importance of the mission to the generation AI and have it output the optimal level of detail of the proposal. This allows for efficient proposals by adjusting the level of detail of the proposal according to the importance of the mission.

[0044] The proposal unit can apply different proposal algorithms depending on the mission category. For example, in the case of an exploration mission, the proposal unit applies an exploration route proposal algorithm. In addition, in the case of a space station construction mission, the proposal unit can also apply a construction plan proposal algorithm. Furthermore, the proposal unit can use the generation AI to apply different proposal algorithms depending on the mission category. For example, the proposal unit can input the mission category to the generation AI and output the optimal proposal algorithm. This makes it possible to make optimal proposals by applying a proposal algorithm depending on the mission category.

[0045] The proposal unit can improve the accuracy of proposals by referring to past proposal results. For example, the proposal unit analyzes past proposal results and improves the proposal algorithm. The proposal unit can also customize the content of the proposal based on past proposal results. Furthermore, the proposal unit can use the generation AI to improve the accuracy of proposals by referring to past proposal results. For example, the proposal unit can input past proposal results into the generation AI and output an algorithm that improves the accuracy of proposals. In this way, the accuracy of proposals is improved by referring to past proposal results.

[0046] The proposal unit can determine the priority of proposals based on the time of submission of the mission. For example, the proposal unit can prioritize proposals for urgent missions. The proposal unit can also prioritize proposals for missions with an approaching submission deadline. Furthermore, the proposal unit can use the generation AI to determine the priority of proposals based on the time of submission of the mission. For example, the proposal unit can input the time of submission of the mission to the generation AI and output the optimal priority of proposals. This enables efficient proposals by determining the priority of proposals based on the time of submission of the mission.

[0047] The suggestion unit can adjust the order of proposals based on the relevance of the missions. For example, the suggestion unit prioritizes proposing highly relevant missions. The suggestion unit can also postpone less relevant missions. Furthermore, the suggestion unit can use the generation AI to adjust the order of proposals based on the relevance of the missions. For example, the suggestion unit can input the relevance of the missions to the generation AI and output the optimal order of proposals. This allows for efficient proposals by adjusting the order of proposals based on the relevance of the missions.

[0048] The suggestion unit can adjust the use of technical terms in the suggestions depending on the user's level of expertise. For example, the suggestion unit can make suggestions in simple language to a user with little expertise. The suggestion unit can also make suggestions using technical terms to a user with a lot of expertise. Furthermore, the suggestion unit can use the generation AI to adjust the use of technical terms in the suggestions depending on the user's level of expertise. For example, the suggestion unit can input the user's level of expertise into the generation AI and output an optimal way of expressing the suggestions. This allows for more appropriate suggestions to be made by adjusting the use of technical terms in the suggestions depending on the user's level of expertise.

[0049] The monitoring unit can detect abnormal values ​​in the vital data and issue an alert immediately. For example, the monitoring unit issues an alert if the heart rate is abnormally high. The monitoring unit can also issue an alert if the blood pressure is abnormally low. Furthermore, the monitoring unit can use the generation AI to detect abnormal values ​​in the vital data and issue an alert immediately. For example, the monitoring unit can input vital data into the generation AI, have it detect abnormal values, and output an alert. This allows for immediate detection of abnormal values ​​in the vital data and issuing an alert, enabling a rapid response.

[0050] The monitoring unit can analyze fluctuations in the work situation in real time and propose appropriate responses. For example, if work efficiency declines, the monitoring unit can suggest a break. The monitoring unit can also suggest support if the workload increases. Furthermore, the monitoring unit can use the generation AI to analyze fluctuations in the work situation in real time and propose appropriate responses. For example, the monitoring unit can input work situation data into the generation AI and have it output the optimal response. This makes it possible to respond appropriately by analyzing fluctuations in the work situation in real time.

[0051] The monitoring unit can predict the current situation by referring to past data. For example, the monitoring unit can predict the current health condition based on past data. The monitoring unit can also predict the current work efficiency based on past work data. Furthermore, the monitoring unit can use the generation AI to predict the current situation by referring to past data. For example, the monitoring unit can input past data into the generation AI and have it predict the current situation. This makes it possible to predict the current situation by referring to past data and take appropriate action.

[0052] The monitoring unit can perform monitoring taking into account geographical location information. For example, if an astronaut is in a specific location, the monitoring unit monitors risks specific to that location. The monitoring unit can also set the monitoring range based on the geographical location information. Furthermore, the monitoring unit can use the generation AI to perform monitoring taking into account geographical location information. For example, the monitoring unit can input geographical location information into the generation AI and have it output the optimal monitoring range. This enables more appropriate monitoring by taking geographical location information into account.

[0053] The monitoring department can improve the accuracy of monitoring by referring to related literature. For example, the monitoring department can set monitoring standards based on related literature. The monitoring department can also improve monitoring methods by referring to related literature. Furthermore, the monitoring department can use the generation AI to improve the accuracy of monitoring by referring to related literature. For example, the monitoring department can input related literature into the generation AI and have it output optimal monitoring standards. In this way, the accuracy of monitoring is improved by referring to related literature.

[0054] The monitoring unit can perform monitoring taking into account the market value of the work. For example, the monitoring unit prioritizes monitoring of work with high market value. The monitoring unit can also postpone work with low market value. Furthermore, the monitoring unit can use the generation AI to perform monitoring taking into account the market value of the work. For example, the monitoring unit can input the market value of the work into the generation AI and have it output the optimal monitoring range. This makes it possible to prioritize monitoring of important work by taking into account the market value of the work.

[0055] The support unit can select the optimal support method by referring to past support data. For example, the support unit selects the optimal support method based on past support data. The support unit can also analyze past support data and improve the support method. Furthermore, the support unit can use the generation AI to select the optimal support method by referring to past support data. For example, the support unit can input past support data into the generation AI and have it output the optimal support method. In this way, the optimal support method can be selected by referring to past support data.

[0056] The support unit can customize support measures based on the current health condition. For example, the support unit can provide advice on appropriate nutritional supplementation based on the current health condition. The support unit can also suggest appropriate rest timing based on the current health condition. Furthermore, the support unit can use the generation AI to customize support measures based on the current health condition. For example, the support unit can input current health condition data into the generation AI and have it output the optimal support measures. This allows for more appropriate support by customizing support measures based on the current health condition.

[0057] The support department can improve the support method by reflecting the feedback. For example, the support department improves the support method based on the feedback. The support department can also customize the content of the support by reflecting the feedback. Furthermore, the support department can use the generation AI to improve the support method by reflecting the feedback. For example, the support department can input feedback data into the generation AI and have it output the optimal support method. In this way, the support method can be optimized by reflecting the feedback.

[0058] The support unit can select the optimal support method by taking geographical location information into consideration. For example, if an astronaut is in a specific location, the support unit will provide support by taking into consideration the risks specific to that location. The support unit can also set the range of support based on the geographical location information. Furthermore, the support unit can use the generation AI to select the optimal support method by taking geographical location information into consideration. For example, the support unit can input geographical location information into the generation AI and have it output the optimal support method. This makes it possible to provide more appropriate support by taking geographical location information into consideration.

[0059] The support department can analyze social media activity and suggest support methods. For example, the support department can analyze the content of social media posts and provide related support information. The support department can also analyze social media trends and provide related support information. Furthermore, the support department can use the generation AI to monitor user activity on social media and provide related support information. For example, the support department can input social media data into the generation AI and have it output the optimal support methods. This allows the support department to provide related support information by analyzing social media activity.

[0060] The support department can customize the support method by reflecting past feedback. For example, the support department improves the support method based on past feedback. The support department can also customize the content of support by reflecting feedback. Furthermore, the support department can use the generation AI to customize the support method by reflecting past feedback. For example, the support department can input past feedback into the generation AI and have it output the optimal support method. In this way, the support method can be optimized by reflecting past feedback.

[0061] The generation unit can optimize the generation algorithm by referring to past evaluations of educational content. For example, the generation unit improves the generation algorithm based on past evaluations of educational content. The generation unit can also customize the content of the generation by referring to past evaluations of educational content. Furthermore, the generation unit can use the generation AI to optimize the generation algorithm by referring to past evaluations of educational content. For example, the generation unit can input evaluation data of past educational content into the generation AI and cause it to output an optimal generation algorithm. In this way, the generation algorithm can be optimized by referring to past evaluations of educational content.

[0062] The generation unit can update the educational content by incorporating the latest research results. For example, the generation unit updates the educational content based on the latest research results. The generation unit can also improve the content of the educational content by incorporating the latest research results. Furthermore, the generation unit can use the generation AI to update the educational content by incorporating the latest research results. For example, the generation unit can input the latest research results data into the generation AI and have it output optimal educational content. This improves the accuracy of the educational content by incorporating the latest research results.

[0063] The generation unit can improve the educational content by reflecting user feedback. For example, the generation unit improves the educational content based on user feedback. The generation unit can also customize the content of the educational content by reflecting the feedback. Furthermore, the generation unit can improve the educational content by using a generation AI by reflecting user feedback. For example, the generation unit can input user feedback data into the generation AI and have it output optimal educational content. In this way, the accuracy of the educational content is improved by reflecting user feedback.

[0064] The generation unit can generate optimal educational content by taking geographical location information into consideration. For example, the generation unit generates region-specific educational content based on geographical location information. The generation unit can also customize the content of the educational content by taking geographical location information into consideration. Furthermore, the generation unit can use a generation AI to generate optimal educational content by taking geographical location information into consideration. For example, the generation unit can input geographical location information to the generation AI and cause it to output optimal educational content. This makes it possible to provide region-specific educational content by taking geographical location information into consideration.

[0065] The generation unit can analyze social media activity and generate relevant educational content. For example, the generation unit can analyze social media posts and generate relevant educational content. The generation unit can also analyze social media trends and generate relevant educational content. Furthermore, the generation unit can use the generation AI to monitor user activity on social media and generate relevant educational content. For example, the generation unit can input social media data into the generation AI and have it output optimal educational content. This makes it possible to provide relevant educational content by analyzing social media activity.

[0066] The generation unit can customize the educational content by reflecting past feedback. For example, the generation unit improves the educational content based on past feedback. The generation unit can also customize the content of the educational content by reflecting feedback. Furthermore, the generation unit can customize the educational content by using a generation AI by reflecting past feedback. For example, the generation unit can input past feedback data into the generation AI and output optimal educational content. In this way, the accuracy of the educational content is improved by reflecting past feedback.

[0067] The provision unit can select the optimal provision method by referring to past provision data. For example, the provision unit selects the optimal provision method based on past provision data. The provision unit can also analyze past provision data and improve the provision method. Furthermore, the provision unit can use the generation AI to select the optimal provision method by referring to past provision data. For example, the provision unit can input past provision data into the generation AI and have it output the optimal provision method. In this way, the optimal provision method can be selected by referring to past provision data.

[0068] The providing unit can customize the means of provision based on the current user situation. For example, the providing unit selects an appropriate provision method based on the current user situation. The providing unit can also customize the content of provision based on the current user situation. Furthermore, the providing unit can customize the means of provision based on the current user situation using the generating AI. For example, the providing unit can input current user situation data into the generating AI and output the optimal means of provision. This enables more appropriate provision by customizing the means of provision based on the current user situation.

[0069] The provision unit can improve the provision method by reflecting the feedback. For example, the provision unit improves the provision method based on the feedback. The provision unit can also customize the content of the provision by reflecting the feedback. Furthermore, the provision unit can use the generation AI to improve the provision method by reflecting the feedback. For example, the provision unit can input feedback data into the generation AI and have it output the optimal provision method. In this way, the provision method can be optimized by reflecting the feedback.

[0070] The provision unit can select the optimal provision method by taking geographical location information into consideration. For example, the provision unit selects a region-specific provision method based on the geographical location information. The provision unit can also customize the content of provision by taking geographical location information into consideration. Furthermore, the provision unit can use the generation AI to select the optimal provision method by taking geographical location information into consideration. For example, the provision unit can input geographical location information into the generation AI and cause it to output the optimal provision method. In this way, a region-specific provision method can be provided by taking geographical location information into consideration.

[0071] The provision unit can analyze social media activity and suggest a means of delivery. For example, the provision unit can analyze the content of social media posts and suggest a related delivery method. The provision unit can also analyze social media trends and suggest a related delivery method. Furthermore, the provision unit can use the generation AI to monitor user activity on social media and suggest a related delivery method. For example, the provision unit can input social media data into the generation AI and have it output the optimal delivery method. In this way, a related delivery method can be proposed by analyzing social media activity.

[0072] The provision unit can customize the provision method by reflecting past feedback. For example, the provision unit improves the provision method based on past feedback. The provision unit can also customize the content of provision by reflecting feedback. Furthermore, the provision unit can use the generation AI to customize the provision method by reflecting past feedback. For example, the provision unit can input past feedback into the generation AI and have it output the optimal provision method. In this way, the provision method can be optimized by reflecting past feedback.

[0073] The analysis unit can optimize the analysis algorithm by referring to past analysis data. For example, the analysis unit improves the analysis algorithm based on past analysis data. The analysis unit can also customize the content of the analysis by referring to past analysis data. Furthermore, the analysis unit can use the generation AI to optimize the analysis algorithm by referring to past analysis data. For example, the analysis unit can input past analysis data into the generation AI and have it output the optimal analysis algorithm. In this way, the analysis algorithm can be optimized by referring to past analysis data.

[0074] The analysis unit can improve the accuracy of the analysis by incorporating the latest observation data. For example, the analysis unit improves the accuracy of the analysis based on the latest observation data. The analysis unit can also improve the content of the analysis by incorporating the latest observation data. Furthermore, the analysis unit can use the generation AI to incorporate the latest observation data to improve the accuracy of the analysis. For example, the analysis unit can input the latest observation data into the generation AI and have it output optimal analysis results. In this way, the accuracy of the analysis is improved by incorporating the latest observation data.

[0075] The analysis unit can improve the analysis method by reflecting user feedback. For example, the analysis unit improves the analysis method based on user feedback. The analysis unit can also customize the content of the analysis by reflecting feedback. Furthermore, the analysis unit can use the generation AI to improve the analysis method by reflecting user feedback. For example, the analysis unit can input user feedback data into the generation AI and have it output the optimal analysis method. In this way, the accuracy of the analysis is improved by reflecting user feedback.

[0076] The analysis unit can perform analysis taking into account geographical location information. For example, the analysis unit sets the range of analysis based on the geographical location information. The analysis unit can also customize the content of the analysis taking into account the geographical location information. Furthermore, the analysis unit can use the generation AI to perform analysis taking into account the geographical location information. For example, the analysis unit can input geographical location information into the generation AI and have it output the optimal analysis range. This enables more appropriate analysis by taking into account the geographical location information.

[0077] The analysis unit can improve the accuracy of the analysis by referring to related literature. For example, the analysis unit sets analysis criteria based on related literature. The analysis unit can also improve the analysis method by referring to related literature. Furthermore, the analysis unit can use the generation AI to improve the accuracy of the analysis by referring to related literature. For example, the analysis unit can input related literature into the generation AI and have it output optimal analysis criteria. In this way, the accuracy of the analysis is improved by referring to related literature.

[0078] The analysis unit can perform analysis taking into account the market value of the observation data. For example, the analysis unit prioritizes analysis of observation data with high market value. The analysis unit can also postpone analysis of observation data with low market value. Furthermore, the analysis unit can use the generation AI to perform analysis taking into account the market value of the observation data. For example, the analysis unit can input the market value of the observation data into the generation AI and have it output the optimal analysis range. This allows important data to be analyzed with priority by taking into account the market value of the observation data.

[0079] The summarization unit can adjust the level of detail of the summary based on the importance of the data. For example, the summarization unit provides a detailed summary for data with high importance. The summarization unit can also provide a concise summary for data with low importance. Furthermore, the summarization unit can use the generation AI to adjust the level of detail of the summary based on the importance of the data. For example, the summarization unit can input the importance of the data to the generation AI and have it output the optimal level of detail of the summary. In this way, an efficient summary can be provided by adjusting the level of detail of the summary according to the importance of the data.

[0080] The summarization unit can apply different summarization algorithms depending on the category of data. For example, in the case of scientific data, the summarization unit can apply a scientific summarization algorithm. In addition, in the case of technical data, the summarization unit can apply a technical summarization algorithm. Furthermore, the summarization unit can use the generation AI to apply different summarization algorithms depending on the category of data. For example, the summarization unit can input the data category to the generation AI and have it output the optimal summarization algorithm. In this way, the optimal summary is provided by applying the summarization algorithm depending on the data category.

[0081] The summarization unit can improve the accuracy of the summarization by referring to past summarization results. For example, the summarization unit analyzes past summarization results and improves the summarization algorithm. The summarization unit can also customize the content of the summary based on past summarization results. Furthermore, the summarization unit can use the generation AI to improve the accuracy of the summarization by referring to past summarization results. For example, the summarization unit can input past summarization results into the generation AI and have it output the optimal summarization algorithm. In this way, the accuracy of the summarization is improved by referring to past summarization results.

[0082] The summarization unit can determine the priority of summaries based on the time of data submission. For example, the summarization unit can prioritize providing summaries in the case of urgent data. The summarization unit can also prioritize providing summaries in the case of data with an approaching submission deadline. Furthermore, the summarization unit can use the generation AI to determine the priority of summaries based on the time of data submission. For example, the summarization unit can input the time of data submission to the generation AI and have it output the optimal priority of summaries. In this way, efficient summaries can be provided by prioritizing summaries based on the time of data submission.

[0083] The summarization unit can adjust the order of summaries based on the relevance of the data. For example, the summarization unit prioritizes summarization of highly relevant data. The summarization unit can also postpone summarization of less relevant data. Furthermore, the summarization unit can adjust the order of summaries based on the relevance of the data using the generation AI. For example, the summarization unit can input the relevance of the data to the generation AI and have it output the optimal order of summaries. In this way, an efficient summary can be provided by adjusting the order of summaries based on the relevance of the data.

[0084] The summarization unit can adjust the use of technical terms in the summary according to the user's level of expertise. For example, the summarization unit can provide a summary in simple language to a user with little expertise. The summarization unit can also provide a summary using technical terms to a user with a lot of expertise. Furthermore, the summarization unit can use the generation AI to adjust the use of technical terms in the summary according to the user's level of expertise. For example, the summarization unit can input the user's level of expertise into the generation AI and have it output an optimal way of expressing the summary. In this way, a more appropriate summary can be provided by adjusting the use of technical terms in the summary according to the user's level of expertise.

[0085] The system according to the embodiment is not limited to the above-described example, and various modifications are possible, for example, as follows.

[0086] The suggestion unit can improve the accuracy of the suggestions by referring to past suggestion results. For example, it can analyze past suggestion results and improve the suggestion algorithm. The suggestion unit can also customize the content of the suggestions based on past suggestion results. Furthermore, the suggestion unit can use a generative AI to improve the accuracy of the suggestions by referring to past suggestion results. In this way, the accuracy of the suggestions is improved by referring to past suggestion results.

[0087] The monitoring unit can predict the current situation by referring to past data. For example, it can predict the current health condition based on past data. The monitoring unit can also predict the current work efficiency based on past work data. Furthermore, the monitoring unit can use generative AI to predict the current situation by referring to past data. This makes it possible to predict the current situation by referring to past data and take appropriate action.

[0088] The support unit can customize support measures based on the current health state. For example, it can provide appropriate nutritional advice based on the current health state. The support unit can also suggest appropriate rest timing based on the current health state. Furthermore, the support unit can use generative AI to customize support measures based on the current health state. This allows for more appropriate support by customizing support measures based on the current health state.

[0089] The generation unit can update the educational content by incorporating the latest research results. For example, the educational content is updated based on the latest research results. The generation unit can also improve the content of the educational content by incorporating the latest research results. Furthermore, the generation unit can update the educational content by incorporating the latest research results using the generative AI. This improves the accuracy of the educational content by incorporating the latest research results.

[0090] The provision unit can select the optimal provision method by referring to past provision data. For example, the optimal provision method is selected based on past provision data. The provision unit can also analyze past provision data and improve the provision method. Furthermore, the provision unit can use the generation AI to select the optimal provision method by referring to past provision data. In this way, the optimal provision method can be selected by referring to past provision data.

[0091] The processing flow of the first embodiment will be briefly explained below.

[0092] Step 1: The collection department collects past mission data and current technological status. For example, they collect data on the success rate of past missions, the technologies used, and environmental conditions, as well as the current technological status of the latest space exploration, communication, and life support technologies. Step 2: The proposal unit analyzes the data collected by the collection unit and proposes an efficient mission plan. For example, it proposes the optimal exploration route and necessary equipment based on past exploration data and the current technology level, and uses generation AI to make proposals for improving the efficiency of the mission. Step 3: The monitoring unit monitors the astronauts' health and work status in real time, including vital data such as heart rate, blood pressure, and oxygen saturation, as well as work status such as progress, type of work, and working hours. Step 4: The support department provides necessary support based on the data obtained by the monitoring department, such as advice on appropriate rest times and nutritional supplementation, as well as medical and psychological support to maintain health. Step 5: The generator generates educational content related to space, such as the history of space, the latest research results, and space exploration technology, and uses AI to generate educational content that is easy for the general public to understand. Step 6: The providing unit provides the educational content generated by the generating unit to the public, for example, through a website, an application, or as a printed material. Step 7: The analysis unit analyzes large amounts of space data, such as astronomical observation data and the results of space exploration, and uses generative AI to analyze the data. Step 8: The summarization section summarizes and provides the important information obtained by the analysis section. For example, it extracts and provides information that is useful to researchers and the general public, and uses generative AI to summarize the data.

[0093] (Example 2) A system according to an embodiment of the present invention utilizes generative AI to support space projects. This system analyzes past mission data and current technological status to propose optimal mission plans. For example, when planning an exploration mission to a specific planet, the generative AI considers past exploration data and current technological levels to propose the optimal exploration route and necessary equipment. It also monitors astronauts' health and work status in real time and provides necessary support. For example, when astronauts work long hours, the generative AI monitors their health and provides advice on appropriate rest times and nutritional supplements. Furthermore, it generates educational content about space and provides it to the general public. For example, it generates videos and articles that clearly explain basic space knowledge and the latest research results. Finally, it analyzes massive amounts of space data and summarizes important information. For example, it analyzes astronomical observation data and space exploration results to extract information useful to researchers and the general public. As a result, the use of generative AI is expected to improve the efficiency and development of space projects. For example, it can support the planning of space exploration missions, support astronauts, provide space education and awareness, and analyze and summarize space data.

[0094] A space business support system according to an embodiment includes a collection unit, a proposal unit, a monitoring unit, a support unit, a generation unit, a provision unit, an analysis unit, and a summarization unit. The collection unit collects past mission data and current technical status. For example, the collection unit can collect data such as the success rate of past missions, the technologies used, and environmental conditions. The collection unit can also collect current technical status such as the latest space exploration technology, communication technology, and life support technology. The proposal unit analyzes the data collected by the collection unit and proposes an efficient mission plan. For example, the proposal unit can propose an optimal exploration route and necessary equipment based on past exploration data and the current technical level. The proposal unit can also make proposals to improve mission efficiency using a generation AI. The monitoring unit monitors the health status and work status of astronauts in real time. For example, the monitoring unit can monitor vital data such as astronauts' heart rate, blood pressure, and oxygen saturation. The monitoring unit can also monitor astronauts' work status such as their progress, type of work, and work time. The support unit provides necessary support based on the data obtained by the monitoring unit. For example, the support unit can provide astronauts with advice on appropriate rest times and nutritional supplementation. The support unit can also provide medical and psychological support to maintain the astronauts' health. The generation unit generates educational content related to space. For example, the generation unit can generate educational content such as the history of space, the latest research results, and space exploration technology. The generation unit can also use generation AI to generate educational content that is easy to understand for the general public. The provision unit provides the educational content generated by the generation unit to the general public. For example, the provision unit can provide the generated educational content through a website or application. The provision unit can also provide the educational content in printed form. The analysis unit analyzes large amounts of space data. For example, the analysis unit can analyze astronomical observation data and the results of space exploration. The analysis unit can also use generation AI to analyze the data. The summarization unit summarizes and provides important information obtained by the analysis unit.For example, the summarization unit can extract and provide information useful to researchers and the general public. The summarization unit can also use a generative AI to summarize data. This allows the space business support system according to the embodiment to utilize generative AI to support space businesses, enabling efficient mission planning, support for astronauts, the generation of educational content, and data analysis and summarization.

[0095] The proposal unit can propose efficient exploration routes and necessary equipment based on past exploration data and the current technology level. For example, the proposal unit analyzes past exploration data and proposes the optimal exploration route by taking into account the success rate of the exploration, the technology used, environmental conditions, etc. The proposal unit can also propose an efficient exploration route using the latest exploration technology, communication technology, and life support technology by taking into account the current technology level. Furthermore, the proposal unit can use a generation AI to optimize the exploration route. For example, the proposal unit can input past exploration data and the current technology level into the generation AI and have it output the optimal exploration route. This allows the optimal mission plan to be proposed by taking into account past data and current technology.

[0096] The monitoring unit can monitor the astronaut's vital data and work status in real time. For example, the monitoring unit monitors vital data such as the astronaut's heart rate, blood pressure, and oxygen saturation in real time. The monitoring unit can also monitor the astronaut's work status, such as the progress, type of work, and work time, in real time. Furthermore, the monitoring unit can use the generation AI to detect abnormalities in the vital data and work status. For example, the monitoring unit can input vital data and work status into the generation AI and have it detect abnormalities. This allows the astronaut's health condition and work status to be monitored in real time, allowing appropriate support to be provided.

[0097] The support unit can provide advice on appropriate rest timing and nutritional supplementation. For example, the support unit can suggest appropriate rest timing based on the astronaut's vital data and work status. The support unit can also provide advice on appropriate nutritional supplementation to maintain the astronaut's health. Furthermore, the support unit can use the generation AI to optimize rest timing and nutritional supplementation. For example, the support unit can input vital data and work status into the generation AI and have it output advice on optimal rest timing and nutritional supplementation. This helps maintain the astronaut's health and support efficient work.

[0098] The generation unit can generate educational content based on space-related knowledge and the latest research results. The generation unit generates educational content such as the history of space, the latest research results, and space exploration technology. The generation unit can also use the generation AI to generate educational content that is easy for the general public to understand. For example, the generation unit can input space-related knowledge and the latest research results into the generation AI and have it output educational content. This allows educational content that reflects the latest research results to be generated and provided to the general public.

[0099] The providing unit can provide the generated educational content to the general public. For example, the providing unit can provide the generated educational content through a website or an application. The providing unit can also provide the educational content as printed material. Furthermore, the providing unit can use the generating AI to optimize the method of providing the educational content. For example, the providing unit can input educational content into the generating AI and have it output the optimal method of providing it. In this way, knowledge about space can be spread by providing the generated educational content to the general public.

[0100] The analysis unit can analyze astronomical observation data and the results of space exploration. For example, the analysis unit analyzes astronomical observation data to extract star positions, luminosity, spectral data, etc. The analysis unit can also analyze the results of space exploration to extract probe observation data, geological data, environmental data, etc. Furthermore, the analysis unit can also analyze data using the generation AI. For example, the analysis unit can input observation data and exploration data into the generation AI and have it extract important information. In this way, important information can be extracted by analyzing the astronomical observation data and the results of space exploration.

[0101] The summarization unit can extract and provide information useful to researchers and the general public. For example, the summarization unit summarizes the data obtained by the analysis unit and extracts information useful to researchers and the general public. The summarization unit can also use a generation AI to summarize data. For example, the summarization unit can input analysis data into the generation AI and have it output a summary. This provides useful information to researchers and the general public, deepening their understanding of space data.

[0102] The collection unit can estimate the user's emotions and adjust the timing of data collection based on the estimated user emotions. For example, if the user is feeling stressed, the collection unit can reduce the frequency of data collection to reduce the burden on the user. Also, if the user is relaxed, the collection unit can increase the frequency of data collection to collect more detailed data. Furthermore, the collection unit can use the generation AI to estimate the user's emotions and adjust the timing of data collection. For example, the collection unit can input the user's emotional data into the generation AI and output the optimal data collection timing. This reduces the burden on the user by adjusting the timing of data collection according to the user's emotions.

[0103] The collection unit can evaluate the reliability of past mission data and prioritize collection of highly reliable data. For example, the collection unit can score the reliability of past mission data and prioritize collection of data with high scores. The collection unit can also filter out low-reliability data and exclude it from collection. Furthermore, the collection unit can use the generation AI to identify highly reliable data sources and prioritize collection of that data. For example, the collection unit can input past mission data into the generation AI and have it evaluate reliability. This improves data accuracy by prioritizing collection of highly reliable data.

[0104] The collection unit can integrate information from different data sources to improve the accuracy of the collection. For example, the collection unit collects the same information from multiple data sources and checks the consistency of the data. The collection unit can also integrate information from different data sources to collect more detailed data. Furthermore, the collection unit can use the generation AI to analyze differences between data sources and improve the accuracy of the collected data. For example, the collection unit can input information from multiple data sources into the generation AI and have it integrate the data. This improves the accuracy of the collected data by integrating information from different data sources.

[0105] The collection unit can select the optimal collection method depending on the type of data to be collected. For example, in the case of text data, the collection unit can efficiently collect the data using an API. In addition, in the case of image data, the collection unit can also collect the data using image recognition technology. Furthermore, in the case of audio data, the collection unit can also collect the data using voice recognition technology. For example, the collection unit can input the type of data to be collected into the generation AI and have it output the optimal collection method. This enables efficient data collection by selecting the optimal collection method depending on the type of data.

[0106] The collection unit can estimate the user's emotions and determine the priority of data to be collected based on the estimated user's emotions. For example, if the user is feeling stressed, the collection unit postpones collection of less important data. Also, if the user is relaxed, the collection unit can prioritize collection of more important data. Furthermore, the collection unit can use the generation AI to estimate the user's emotions and determine the priority of data to be collected. For example, the collection unit can input the user's emotional data into the generation AI and output the priority of collected data. In this way, important data can be collected preferentially by determining the priority of data according to the user's emotions.

[0107] The collection unit can prioritize collection of highly relevant data by taking geographical location information into consideration. For example, the collection unit can prioritize collection of data from a specific region to obtain information specific to the region. The collection unit can also filter highly relevant data based on geographical location information. Furthermore, the collection unit can use the generation AI to set the range of data collection by taking geographical location information into consideration. For example, the collection unit can input geographical location information into the generation AI and have it output highly relevant data. This allows highly relevant data to be collected efficiently by taking geographical location information into consideration.

[0108] The collection unit can analyze social media activities and collect related data. For example, the collection unit analyzes the content of social media posts and collects related data. The collection unit can also analyze social media trends and collect related data. Furthermore, the collection unit can use the generation AI to monitor social media user activities and collect related data. For example, the collection unit can input social media data into the generation AI and cause it to output related data. This allows related data to be collected efficiently by analyzing social media activities.

[0109] The collection unit can customize the collection method by reflecting past feedback. For example, the collection unit can improve data collection methods based on past feedback. The collection unit can also select data to collect by reflecting feedback. Furthermore, the collection unit can use the generation AI to adjust the frequency and timing of collection based on feedback. For example, the collection unit can input past feedback into the generation AI and have it output the optimal collection method. In this way, the collection method can be optimized by reflecting past feedback.

[0110] The suggestion unit can estimate the user's emotions and adjust the way suggestions are expressed based on the estimated user's emotions. For example, if the user is feeling stressed, the suggestion unit can make simple and easy-to-understand suggestions. Also, if the user is relaxed, the suggestion unit can make suggestions that include detailed information. Furthermore, the suggestion unit can use the generation AI to estimate the user's emotions and adjust the way suggestions are expressed. For example, the suggestion unit can input user emotion data into the generation AI and output the optimal way to express suggestions. This allows more appropriate suggestions to be made by adjusting the way suggestions are expressed according to the user's emotions.

[0111] The suggestion unit can adjust the level of detail of the proposal based on the importance of the mission. For example, the suggestion unit makes a detailed proposal for a mission of high importance. The suggestion unit can also make a concise proposal for a mission of low importance. Furthermore, the suggestion unit can use the generation AI to adjust the level of detail of the proposal based on the importance of the mission. For example, the suggestion unit can input the importance of the mission to the generation AI and have it output the optimal level of detail of the proposal. This allows for efficient proposals by adjusting the level of detail of the proposal according to the importance of the mission.

[0112] The proposal unit can apply different proposal algorithms depending on the mission category. For example, in the case of an exploration mission, the proposal unit applies an exploration route proposal algorithm. In addition, in the case of a space station construction mission, the proposal unit can also apply a construction plan proposal algorithm. Furthermore, the proposal unit can use the generation AI to apply different proposal algorithms depending on the mission category. For example, the proposal unit can input the mission category to the generation AI and output the optimal proposal algorithm. This makes it possible to make optimal proposals by applying a proposal algorithm depending on the mission category.

[0113] The proposal unit can improve the accuracy of proposals by referring to past proposal results. For example, the proposal unit analyzes past proposal results and improves the proposal algorithm. The proposal unit can also customize the content of the proposal based on past proposal results. Furthermore, the proposal unit can use the generation AI to improve the accuracy of proposals by referring to past proposal results. For example, the proposal unit can input past proposal results into the generation AI and output an algorithm that improves the accuracy of proposals. In this way, the accuracy of proposals is improved by referring to past proposal results.

[0114] The suggestion unit can estimate the user's emotions and adjust the length of the suggestions based on the estimated user's emotions. For example, if the user is feeling stressed, the suggestion unit can make short, to-the-point suggestions. Alternatively, if the user is relaxed, the suggestion unit can make longer suggestions with detailed explanations. Furthermore, the suggestion unit can use the generation AI to estimate the user's emotions and adjust the length of the suggestions. For example, the suggestion unit can input the user's emotion data into the generation AI and output the optimal length of the suggestions. This allows for more appropriate suggestions to be made by adjusting the length of the suggestions according to the user's emotions.

[0115] The proposal unit can determine the priority of proposals based on the time of submission of the mission. For example, the proposal unit can prioritize proposals for urgent missions. The proposal unit can also prioritize proposals for missions with an approaching submission deadline. Furthermore, the proposal unit can use the generation AI to determine the priority of proposals based on the time of submission of the mission. For example, the proposal unit can input the time of submission of the mission to the generation AI and output the optimal priority of proposals. This enables efficient proposals by determining the priority of proposals based on the time of submission of the mission.

[0116] The suggestion unit can adjust the order of proposals based on the relevance of the missions. For example, the suggestion unit prioritizes proposing highly relevant missions. The suggestion unit can also postpone less relevant missions. Furthermore, the suggestion unit can use the generation AI to adjust the order of proposals based on the relevance of the missions. For example, the suggestion unit can input the relevance of the missions to the generation AI and output the optimal order of proposals. This allows for efficient proposals by adjusting the order of proposals based on the relevance of the missions.

[0117] The suggestion unit can adjust the use of technical terms in the suggestions depending on the user's level of expertise. For example, the suggestion unit can make suggestions in simple language to a user with little expertise. The suggestion unit can also make suggestions using technical terms to a user with a lot of expertise. Furthermore, the suggestion unit can use the generation AI to adjust the use of technical terms in the suggestions depending on the user's level of expertise. For example, the suggestion unit can input the user's level of expertise into the generation AI and output an optimal way of expressing the suggestions. This allows for more appropriate suggestions to be made by adjusting the use of technical terms in the suggestions depending on the user's level of expertise.

[0118] The monitoring unit can estimate the user's emotions and adjust the monitoring standards based on the estimated user's emotions. For example, the monitoring unit can reduce the frequency of monitoring when the user is feeling stressed. The monitoring unit can also increase the frequency of monitoring when the user is relaxed. Furthermore, the monitoring unit can use the generation AI to estimate the user's emotions and adjust the monitoring standards. For example, the monitoring unit can input the user's emotional data into the generation AI and have it output optimal monitoring standards. This allows for more appropriate monitoring by adjusting the monitoring standards according to the user's emotions.

[0119] The monitoring unit can detect abnormal values ​​in the vital data and issue an alert immediately. For example, the monitoring unit issues an alert if the heart rate is abnormally high. The monitoring unit can also issue an alert if the blood pressure is abnormally low. Furthermore, the monitoring unit can use the generation AI to detect abnormal values ​​in the vital data and issue an alert immediately. For example, the monitoring unit can input vital data into the generation AI, have it detect abnormal values, and output an alert. This allows for immediate detection of abnormal values ​​in the vital data and issuing an alert, enabling a rapid response.

[0120] The monitoring unit can analyze fluctuations in the work situation in real time and propose appropriate responses. For example, if work efficiency declines, the monitoring unit can suggest a break. The monitoring unit can also suggest support if the workload increases. Furthermore, the monitoring unit can use the generation AI to analyze fluctuations in the work situation in real time and propose appropriate responses. For example, the monitoring unit can input work situation data into the generation AI and have it output the optimal response. This makes it possible to respond appropriately by analyzing fluctuations in the work situation in real time.

[0121] The monitoring unit can predict the current situation by referring to past data. For example, the monitoring unit can predict the current health condition based on past data. The monitoring unit can also predict the current work efficiency based on past work data. Furthermore, the monitoring unit can use the generation AI to predict the current situation by referring to past data. For example, the monitoring unit can input past data into the generation AI and have it predict the current situation. This makes it possible to predict the current situation by referring to past data and take appropriate action.

[0122] The monitoring unit can estimate the user's emotions and adjust the order in which the monitoring results are displayed based on the estimated user's emotions. For example, if the user is feeling stressed, the monitoring unit can prioritize displaying important results. The monitoring unit can also display detailed results if the user is relaxed. Furthermore, the monitoring unit can use the generation AI to estimate the user's emotions and adjust the order in which the monitoring results are displayed. For example, the monitoring unit can input the user's emotional data into the generation AI and output the optimal display order. This allows important information to be provided preferentially by adjusting the order in which the monitoring results are displayed according to the user's emotions.

[0123] The monitoring unit can perform monitoring taking into account geographical location information. For example, if an astronaut is in a specific location, the monitoring unit monitors risks specific to that location. The monitoring unit can also set the monitoring range based on the geographical location information. Furthermore, the monitoring unit can use the generation AI to perform monitoring taking into account geographical location information. For example, the monitoring unit can input geographical location information into the generation AI and have it output the optimal monitoring range. This enables more appropriate monitoring by taking geographical location information into account.

[0124] The monitoring department can improve the accuracy of monitoring by referring to related literature. For example, the monitoring department can set monitoring standards based on related literature. The monitoring department can also improve monitoring methods by referring to related literature. Furthermore, the monitoring department can use the generation AI to improve the accuracy of monitoring by referring to related literature. For example, the monitoring department can input related literature into the generation AI and have it output optimal monitoring standards. In this way, the accuracy of monitoring is improved by referring to related literature.

[0125] The monitoring unit can perform monitoring taking into account the market value of the work. For example, the monitoring unit prioritizes monitoring of work with high market value. The monitoring unit can also postpone work with low market value. Furthermore, the monitoring unit can use the generation AI to perform monitoring taking into account the market value of the work. For example, the monitoring unit can input the market value of the work into the generation AI and have it output the optimal monitoring range. This makes it possible to prioritize monitoring of important work by taking into account the market value of the work.

[0126] The support unit can estimate the user's emotions and adjust the support method based on the estimated user's emotions. For example, if the user is feeling stressed, the support unit can provide a support method that will help the user relax. Also, if the user is relaxed, the support unit can provide detailed support information. Furthermore, the support unit can use the generation AI to estimate the user's emotions and adjust the support method. For example, the support unit can input the user's emotion data into the generation AI and have it output the optimal support method. This allows for more appropriate support by adjusting the support method according to the user's emotions.

[0127] The support unit can select the optimal support method by referring to past support data. For example, the support unit selects the optimal support method based on past support data. The support unit can also analyze past support data and improve the support method. Furthermore, the support unit can use the generation AI to select the optimal support method by referring to past support data. For example, the support unit can input past support data into the generation AI and have it output the optimal support method. In this way, the optimal support method can be selected by referring to past support data.

[0128] The support unit can customize support measures based on the current health condition. For example, the support unit can provide advice on appropriate nutritional supplementation based on the current health condition. The support unit can also suggest appropriate rest timing based on the current health condition. Furthermore, the support unit can use the generation AI to customize support measures based on the current health condition. For example, the support unit can input current health condition data into the generation AI and have it output the optimal support measures. This allows for more appropriate support by customizing support measures based on the current health condition.

[0129] The support department can improve the support method by reflecting the feedback. For example, the support department improves the support method based on the feedback. The support department can also customize the content of the support by reflecting the feedback. Furthermore, the support department can use the generation AI to improve the support method by reflecting the feedback. For example, the support department can input feedback data into the generation AI and have it output the optimal support method. In this way, the support method can be optimized by reflecting the feedback.

[0130] The support unit can estimate the user's emotions and determine the priority of support based on the estimated user's emotions. For example, the support unit can provide support preferentially when the user is feeling stressed. The support unit can also provide detailed support information when the user is relaxed. Furthermore, the support unit can use the generation AI to estimate the user's emotions and determine the priority of support. For example, the support unit can input the user's emotion data into the generation AI and output the optimal priority of support. This allows important support to be provided preferentially by determining the priority of support according to the user's emotions.

[0131] The support unit can select the optimal support method by taking geographical location information into consideration. For example, if an astronaut is in a specific location, the support unit will provide support by taking into consideration the risks specific to that location. The support unit can also set the range of support based on the geographical location information. Furthermore, the support unit can use the generation AI to select the optimal support method by taking geographical location information into consideration. For example, the support unit can input geographical location information into the generation AI and have it output the optimal support method. This makes it possible to provide more appropriate support by taking geographical location information into consideration.

[0132] The support department can analyze social media activity and suggest support methods. For example, the support department can analyze the content of social media posts and provide related support information. The support department can also analyze social media trends and provide related support information. Furthermore, the support department can use the generation AI to monitor user activity on social media and provide related support information. For example, the support department can input social media data into the generation AI and have it output the optimal support methods. This allows the support department to provide related support information by analyzing social media activity.

[0133] The support department can customize the support method by reflecting past feedback. For example, the support department improves the support method based on past feedback. The support department can also customize the content of support by reflecting feedback. Furthermore, the support department can use the generation AI to customize the support method by reflecting past feedback. For example, the support department can input past feedback into the generation AI and have it output the optimal support method. In this way, the support method can be optimized by reflecting past feedback.

[0134] The generation unit can estimate the user's emotions and adjust the educational content generation method based on the estimated user's emotions. For example, if the user is feeling stressed, the generation unit generates simple and easy-to-understand educational content. Also, if the user is relaxed, the generation unit can generate educational content that includes detailed information. Furthermore, the generation unit can use a generation AI to estimate the user's emotions and adjust the educational content generation method. For example, the generation unit can input user emotion data into the generation AI and output an optimal educational content generation method. In this way, more appropriate educational content can be generated by adjusting the educational content generation method according to the user's emotions.

[0135] The generation unit can optimize the generation algorithm by referring to past evaluations of educational content. For example, the generation unit improves the generation algorithm based on past evaluations of educational content. The generation unit can also customize the content of the generation by referring to past evaluations of educational content. Furthermore, the generation unit can use the generation AI to optimize the generation algorithm by referring to past evaluations of educational content. For example, the generation unit can input evaluation data of past educational content into the generation AI and cause it to output an optimal generation algorithm. In this way, the generation algorithm can be optimized by referring to past evaluations of educational content.

[0136] The generation unit can update the educational content by incorporating the latest research results. For example, the generation unit updates the educational content based on the latest research results. The generation unit can also improve the content of the educational content by incorporating the latest research results. Furthermore, the generation unit can use the generation AI to update the educational content by incorporating the latest research results. For example, the generation unit can input the latest research results data into the generation AI and have it output optimal educational content. This improves the accuracy of the educational content by incorporating the latest research results.

[0137] The generation unit can improve the educational content by reflecting user feedback. For example, the generation unit improves the educational content based on user feedback. The generation unit can also customize the content of the educational content by reflecting the feedback. Furthermore, the generation unit can improve the educational content by using a generation AI by reflecting user feedback. For example, the generation unit can input user feedback data into the generation AI and have it output optimal educational content. In this way, the accuracy of the educational content is improved by reflecting user feedback.

[0138] The generation unit can estimate the user's emotions and determine the priority of educational content based on the estimated user's emotions. For example, if the user is feeling stressed, the generation unit postpones content of low importance. The generation unit can also provide content of high importance preferentially if the user is relaxed. Furthermore, the generation unit can use a generation AI to estimate the user's emotions and determine the priority of educational content. For example, the generation unit can input user emotion data into the generation AI and output the optimal priority of educational content. This allows important content to be provided preferentially by determining the priority of educational content according to the user's emotions.

[0139] The generation unit can generate optimal educational content by taking geographical location information into consideration. For example, the generation unit generates region-specific educational content based on geographical location information. The generation unit can also customize the content of the educational content by taking geographical location information into consideration. Furthermore, the generation unit can use a generation AI to generate optimal educational content by taking geographical location information into consideration. For example, the generation unit can input geographical location information to the generation AI and cause it to output optimal educational content. This makes it possible to provide region-specific educational content by taking geographical location information into consideration.

[0140] The generation unit can analyze social media activity and generate relevant educational content. For example, the generation unit can analyze social media posts and generate relevant educational content. The generation unit can also analyze social media trends and generate relevant educational content. Furthermore, the generation unit can use the generation AI to monitor user activity on social media and generate relevant educational content. For example, the generation unit can input social media data into the generation AI and have it output optimal educational content. This makes it possible to provide relevant educational content by analyzing social media activity.

[0141] The generation unit can customize the educational content by reflecting past feedback. For example, the generation unit improves the educational content based on past feedback. The generation unit can also customize the content of the educational content by reflecting feedback. Furthermore, the generation unit can customize the educational content by using a generation AI by reflecting past feedback. For example, the generation unit can input past feedback data into the generation AI and output optimal educational content. In this way, the accuracy of the educational content is improved by reflecting past feedback.

[0142] The providing unit can estimate the user's emotions and adjust the content delivery method based on the estimated user's emotions. For example, if the user is feeling stressed, the providing unit selects a simple and easy-to-understand delivery method. Also, if the user is relaxed, the providing unit can select a delivery method that includes detailed information. Furthermore, the providing unit can use the generating AI to estimate the user's emotions and adjust the content delivery method. For example, the providing unit can input user emotion data into the generating AI and output the optimal delivery method. This allows for more appropriate delivery by adjusting the content delivery method according to the user's emotions.

[0143] The provision unit can select the optimal provision method by referring to past provision data. For example, the provision unit selects the optimal provision method based on past provision data. The provision unit can also analyze past provision data and improve the provision method. Furthermore, the provision unit can use the generation AI to select the optimal provision method by referring to past provision data. For example, the provision unit can input past provision data into the generation AI and have it output the optimal provision method. In this way, the optimal provision method can be selected by referring to past provision data.

[0144] The providing unit can customize the means of provision based on the current user situation. For example, the providing unit selects an appropriate provision method based on the current user situation. The providing unit can also customize the content of provision based on the current user situation. Furthermore, the providing unit can customize the means of provision based on the current user situation using the generating AI. For example, the providing unit can input current user situation data into the generating AI and output the optimal means of provision. This enables more appropriate provision by customizing the means of provision based on the current user situation.

[0145] The provision unit can improve the provision method by reflecting the feedback. For example, the provision unit improves the provision method based on the feedback. The provision unit can also customize the content of the provision by reflecting the feedback. Furthermore, the provision unit can use the generation AI to improve the provision method by reflecting the feedback. For example, the provision unit can input feedback data into the generation AI and have it output the optimal provision method. In this way, the provision method can be optimized by reflecting the feedback.

[0146] The providing unit can estimate the user's emotions and determine the priority of content to be provided based on the estimated user's emotions. For example, if the user is feeling stressed, the providing unit postpones content of low importance. Also, if the user is relaxed, the providing unit can provide content of high importance preferentially. Furthermore, the providing unit can use the generating AI to estimate the user's emotions and determine the priority of content to be provided. For example, the providing unit can input user emotion data into the generating AI and output the priority of optimal content. In this way, by determining the priority of content to be provided according to the user's emotions, important content can be provided preferentially.

[0147] The provision unit can select the optimal provision method by taking geographical location information into consideration. For example, the provision unit selects a region-specific provision method based on the geographical location information. The provision unit can also customize the content of provision by taking geographical location information into consideration. Furthermore, the provision unit can use the generation AI to select the optimal provision method by taking geographical location information into consideration. For example, the provision unit can input geographical location information into the generation AI and cause it to output the optimal provision method. In this way, a region-specific provision method can be provided by taking geographical location information into consideration.

[0148] The provision unit can analyze social media activity and suggest a means of delivery. For example, the provision unit can analyze the content of social media posts and suggest a related delivery method. The provision unit can also analyze social media trends and suggest a related delivery method. Furthermore, the provision unit can use the generation AI to monitor user activity on social media and suggest a related delivery method. For example, the provision unit can input social media data into the generation AI and have it output the optimal delivery method. In this way, a related delivery method can be proposed by analyzing social media activity.

[0149] The provision unit can customize the provision method by reflecting past feedback. For example, the provision unit improves the provision method based on past feedback. The provision unit can also customize the content of provision by reflecting feedback. Furthermore, the provision unit can use the generation AI to customize the provision method by reflecting past feedback. For example, the provision unit can input past feedback into the generation AI and have it output the optimal provision method. In this way, the provision method can be optimized by reflecting past feedback.

[0150] The analysis unit can estimate the user's emotions and adjust the analysis criteria based on the estimated user emotions. For example, the analysis unit can reduce the frequency of analysis when the user is feeling stressed. The analysis unit can also increase the frequency of analysis when the user is relaxed. Furthermore, the analysis unit can use the generation AI to estimate the user's emotions and adjust the analysis criteria. For example, the analysis unit can input the user's emotional data into the generation AI and have it output the optimal analysis criteria. This allows for more appropriate analysis by adjusting the analysis criteria according to the user's emotions.

[0151] The analysis unit can optimize the analysis algorithm by referring to past analysis data. For example, the analysis unit improves the analysis algorithm based on past analysis data. The analysis unit can also customize the content of the analysis by referring to past analysis data. Furthermore, the analysis unit can use the generation AI to optimize the analysis algorithm by referring to past analysis data. For example, the analysis unit can input past analysis data into the generation AI and have it output the optimal analysis algorithm. In this way, the analysis algorithm can be optimized by referring to past analysis data.

[0152] The analysis unit can improve the accuracy of the analysis by incorporating the latest observation data. For example, the analysis unit improves the accuracy of the analysis based on the latest observation data. The analysis unit can also improve the content of the analysis by incorporating the latest observation data. Furthermore, the analysis unit can use the generation AI to incorporate the latest observation data to improve the accuracy of the analysis. For example, the analysis unit can input the latest observation data into the generation AI and have it output optimal analysis results. In this way, the accuracy of the analysis is improved by incorporating the latest observation data.

[0153] The analysis unit can improve the analysis method by reflecting user feedback. For example, the analysis unit improves the analysis method based on user feedback. The analysis unit can also customize the content of the analysis by reflecting feedback. Furthermore, the analysis unit can use the generation AI to improve the analysis method by reflecting user feedback. For example, the analysis unit can input user feedback data into the generation AI and have it output the optimal analysis method. In this way, the accuracy of the analysis is improved by reflecting user feedback.

[0154] The analysis unit can estimate the user's emotions and adjust the display method of the analysis results based on the estimated user emotions. For example, if the user is feeling stressed, the analysis unit can prioritize displaying important results. The analysis unit can also display detailed results if the user is relaxed. Furthermore, the analysis unit can use the generation AI to estimate the user's emotions and adjust the display method of the analysis results. For example, the analysis unit can input user emotion data into the generation AI and have it output the optimal display method. This allows important information to be provided preferentially by adjusting the display method of the analysis results according to the user's emotions.

[0155] The analysis unit can perform analysis taking into account geographical location information. For example, the analysis unit sets the range of analysis based on the geographical location information. The analysis unit can also customize the content of the analysis taking into account the geographical location information. Furthermore, the analysis unit can use the generation AI to perform analysis taking into account the geographical location information. For example, the analysis unit can input geographical location information into the generation AI and have it output the optimal analysis range. This enables more appropriate analysis by taking into account the geographical location information.

[0156] The analysis unit can improve the accuracy of the analysis by referring to related literature. For example, the analysis unit sets analysis criteria based on related literature. The analysis unit can also improve the analysis method by referring to related literature. Furthermore, the analysis unit can use the generation AI to improve the accuracy of the analysis by referring to related literature. For example, the analysis unit can input related literature into the generation AI and have it output optimal analysis criteria. In this way, the accuracy of the analysis is improved by referring to related literature.

[0157] The analysis unit can perform analysis taking into account the market value of the observation data. For example, the analysis unit prioritizes analysis of observation data with high market value. The analysis unit can also postpone analysis of observation data with low market value. Furthermore, the analysis unit can use the generation AI to perform analysis taking into account the market value of the observation data. For example, the analysis unit can input the market value of the observation data into the generation AI and have it output the optimal analysis range. This allows important data to be analyzed with priority by taking into account the market value of the observation data.

[0158] The summarization unit can estimate the user's emotions and adjust the way the summary is presented based on the estimated user's emotions. For example, if the user is feeling stressed, the summarization unit can provide a simple and easy-to-understand summary. Also, if the user is relaxed, the summarization unit can provide a summary that includes detailed information. Furthermore, the summarization unit can use the generation AI to estimate the user's emotions and adjust the way the summary is presented. For example, the summarization unit can input user emotion data into the generation AI and have it output the optimal way to present the summary. This allows the summary to be provided more appropriately by adjusting the way the summary is presented according to the user's emotions.

[0159] The summarization unit can adjust the level of detail of the summary based on the importance of the data. For example, the summarization unit provides a detailed summary for data with high importance. The summarization unit can also provide a concise summary for data with low importance. Furthermore, the summarization unit can use the generation AI to adjust the level of detail of the summary based on the importance of the data. For example, the summarization unit can input the importance of the data to the generation AI and have it output the optimal level of detail of the summary. In this way, an efficient summary can be provided by adjusting the level of detail of the summary according to the importance of the data.

[0160] The summarization unit can apply different summarization algorithms depending on the category of data. For example, in the case of scientific data, the summarization unit can apply a scientific summarization algorithm. In addition, in the case of technical data, the summarization unit can apply a technical summarization algorithm. Furthermore, the summarization unit can use the generation AI to apply different summarization algorithms depending on the category of data. For example, the summarization unit can input the data category to the generation AI and have it output the optimal summarization algorithm. In this way, the optimal summary is provided by applying the summarization algorithm depending on the data category.

[0161] The summarization unit can improve the accuracy of the summarization by referring to past summarization results. For example, the summarization unit analyzes past summarization results and improves the summarization algorithm. The summarization unit can also customize the content of the summary based on past summarization results. Furthermore, the summarization unit can use the generation AI to improve the accuracy of the summarization by referring to past summarization results. For example, the summarization unit can input past summarization results into the generation AI and have it output the optimal summarization algorithm. In this way, the accuracy of the summarization is improved by referring to past summarization results.

[0162] The summarization unit can estimate the user's emotions and adjust the length of the summary based on the estimated user emotions. For example, if the user is feeling stressed, the summarization unit can provide a short, to-the-point summary. Alternatively, if the user is relaxed, the summarization unit can provide a longer summary with detailed explanations. Furthermore, the summarization unit can use the generation AI to estimate the user's emotions and adjust the length of the summary. For example, the summarization unit can input user emotion data into the generation AI and have it output the optimal length of the summary. This allows the length of the summary to be adjusted according to the user's emotions, thereby providing a more appropriate summary.

[0163] The summarization unit can determine the priority of summaries based on the time of data submission. For example, the summarization unit can prioritize providing summaries in the case of urgent data. The summarization unit can also prioritize providing summaries in the case of data with an approaching submission deadline. Furthermore, the summarization unit can use the generation AI to determine the priority of summaries based on the time of data submission. For example, the summarization unit can input the time of data submission to the generation AI and have it output the optimal priority of summaries. In this way, efficient summaries can be provided by prioritizing summaries based on the time of data submission.

[0164] The summarization unit can adjust the order of summaries based on the relevance of the data. For example, the summarization unit prioritizes summarization of highly relevant data. The summarization unit can also postpone summarization of less relevant data. Furthermore, the summarization unit can adjust the order of summaries based on the relevance of the data using the generation AI. For example, the summarization unit can input the relevance of the data to the generation AI and have it output the optimal order of summaries. In this way, an efficient summary can be provided by adjusting the order of summaries based on the relevance of the data.

[0165] The summarization unit can adjust the use of technical terms in the summary according to the user's level of expertise. For example, the summarization unit can provide a summary in simple language to a user with little expertise. The summarization unit can also provide a summary using technical terms to a user with a lot of expertise. Furthermore, the summarization unit can use the generation AI to adjust the use of technical terms in the summary according to the user's level of expertise. For example, the summarization unit can input the user's level of expertise into the generation AI and have it output an optimal way of expressing the summary. In this way, a more appropriate summary can be provided by adjusting the use of technical terms in the summary according to the user's level of expertise. === Hard Collateral 1-1 === Each of the multiple elements, including the collection unit, suggestion unit, monitoring unit, support unit, generation unit, provision unit, analysis unit, and summarization unit, is realized, for example, by at least one of the smart device 14 and the data processing device 12. For example, the collection unit collects data using the camera 42 and microphone 38B of the smart device 14, which is analyzed by the specific processing unit 290 of the data processing device 12. The suggestion unit is realized by the specific processing unit 290 of the data processing device 12 and proposes an optimal mission plan based on the collected data. The monitoring unit monitors the astronaut's health using sensors of the smart device 14, which is analyzed by the specific processing unit 290 of the data processing device 12. The support unit is realized by the specific processing unit 290 of the data processing device 12 and provides appropriate support based on the data from the monitoring unit. The generation unit generates educational content using the control unit 46A of the smart device 14, and the provision unit provides the generated content through the output device 40 of the smart device 14. The analysis unit analyzes a large amount of space data using the specific processing unit 290 of the data processing device 12, and the summarization unit summarizes and provides the analysis results. === Hard Collateral 1-2 === Each of the multiple elements, including the collection unit, suggestion unit, monitoring unit, support unit, generation unit, provision unit, analysis unit, and summarization unit, is realized, for example, by at least one of the smart glasses 214 and the data processing device 12. For example, the collection unit collects data using the camera 42 and microphone 238 of the smart glasses 214, which is analyzed by the specific processing unit 290 of the data processing device 12. The suggestion unit is realized by the specific processing unit 290 of the data processing device 12 and proposes an optimal mission plan based on the collected data. The monitoring unit monitors the astronaut's health status using sensors in the smart glasses 214, which is analyzed by the specific processing unit 290 of the data processing device 12. The support unit is realized by the specific processing unit 290 of the data processing device 12 and provides appropriate support based on data from the monitoring unit. The generation unit generates educational content using the control unit 46A of the smart glasses 214, and the provision unit provides the generated content through the output device of the smart glasses 214. The analysis unit analyzes a large amount of space data using the specific processing unit 290 of the data processing device 12, and the summarization unit summarizes and provides the analysis results. === Hard Collateral 1-3 === Each of the multiple elements, including the collection unit, suggestion unit, monitoring unit, support unit, generation unit, provision unit, analysis unit, and summarization unit, is realized, for example, by at least one of the headset terminal 314 and the data processing device 12. For example, the collection unit collects data using the camera 42 and microphone 238 of the headset terminal 314, and the data is analyzed by the specific processing unit 290 of the data processing device 12. The suggestion unit is realized by the specific processing unit 290 of the data processing device 12 and proposes an optimal mission plan based on the collected data. The monitoring unit monitors the astronaut's health using sensors in the headset terminal 314, and the data is analyzed by the specific processing unit 290 of the data processing device 12. The support unit is realized by the specific processing unit 290 of the data processing device 12 and provides appropriate support based on data from the monitoring unit. The generation unit generates educational content using the control unit 46A of the headset terminal 314, and the provision unit provides the generated content through the output device of the headset terminal 314. The analysis unit analyzes a large amount of space data using the specific processing unit 290 of the data processing device 12, and the summarization unit summarizes and provides the analysis results. === Hard Collateral 1-4 === Each of the multiple elements, including the collection unit, suggestion unit, monitoring unit, support unit, generation unit, provision unit, analysis unit, and summarization unit, is realized, for example, by at least one of the robot 414 and the data processing device 12. For example, the collection unit collects data using the camera 42 and microphone 238 of the robot 414, which is analyzed by the specific processing unit 290 of the data processing device 12. The suggestion unit is realized by the specific processing unit 290 of the data processing device 12 and proposes an optimal mission plan based on the collected data. The monitoring unit monitors the astronaut's health using the sensors of the robot 414, which is analyzed by the specific processing unit 290 of the data processing device 12. The support unit is realized by the specific processing unit 290 of the data processing device 12 and provides appropriate support based on data from the monitoring unit. The generation unit generates educational content using the control unit 46A of the robot 414, and the provision unit provides the generated content through the output device of the robot 414. The analysis unit analyzes large amounts of space data using the specific processing unit 290 of the data processing device 12, and the summarization unit summarizes and provides the analysis results.

[0166] The system according to the embodiment is not limited to the above-described example, and various modifications are possible, for example, as follows.

[0167] The suggestion unit can estimate the user's emotions and adjust the way suggestions are expressed based on the estimated user emotions. For example, if the user is feeling stressed, it can make simple and easy-to-understand suggestions. On the other hand, if the user is relaxed, it can make suggestions that include detailed information. Furthermore, the suggestion unit can use a generation AI to estimate the user's emotions and adjust the way suggestions are expressed. This allows for more appropriate suggestions to be made by adjusting the way suggestions are expressed according to the user's emotions.

[0168] The monitoring unit can estimate the user's emotions and adjust the monitoring standards based on the estimated user emotions. For example, if the user is feeling stressed, the monitoring frequency can be reduced. Alternatively, if the user is relaxed, the monitoring frequency can be increased. Furthermore, the monitoring unit can use generative AI to estimate the user's emotions and adjust the monitoring standards. This allows for more appropriate monitoring by adjusting the monitoring standards according to the user's emotions.

[0169] The support unit can estimate the user's emotions and adjust the support method based on the estimated user emotions. For example, if the user is feeling stressed, it can provide a support method that helps the user relax. Also, if the user is relaxed, it can provide detailed support information. Furthermore, the support unit can use the generation AI to estimate the user's emotions and adjust the support method. This allows for more appropriate support by adjusting the support method according to the user's emotions.

[0170] The generation unit can estimate the user's emotions and adjust the method for generating educational content based on the estimated user's emotions. For example, if the user is feeling stressed, simple and easy-to-understand educational content can be generated. Alternatively, if the user is relaxed, educational content containing detailed information can be generated. Furthermore, the generation unit can use the generation AI to estimate the user's emotions and adjust the method for generating educational content. In this way, more appropriate educational content can be generated by adjusting the method for generating educational content according to the user's emotions.

[0171] The providing unit can estimate the user's emotions and adjust the content delivery method based on the estimated user's emotions. For example, if the user is feeling stressed, a simple and easy-to-understand delivery method can be selected. Alternatively, if the user is relaxed, a delivery method including detailed information can be selected. Furthermore, the providing unit can use the generation AI to estimate the user's emotions and adjust the content delivery method. This allows for more appropriate delivery by adjusting the content delivery method according to the user's emotions.

[0172] The suggestion unit can improve the accuracy of the suggestions by referring to past suggestion results. For example, it can analyze past suggestion results and improve the suggestion algorithm. The suggestion unit can also customize the content of the suggestions based on past suggestion results. Furthermore, the suggestion unit can use a generative AI to improve the accuracy of the suggestions by referring to past suggestion results. In this way, the accuracy of the suggestions is improved by referring to past suggestion results.

[0173] The monitoring unit can predict the current situation by referring to past data. For example, it can predict the current health condition based on past data. The monitoring unit can also predict the current work efficiency based on past work data. Furthermore, the monitoring unit can use generative AI to predict the current situation by referring to past data. This makes it possible to predict the current situation by referring to past data and take appropriate action.

[0174] The support unit can customize support measures based on the current health state. For example, it can provide appropriate nutritional advice based on the current health state. The support unit can also suggest appropriate rest timing based on the current health state. Furthermore, the support unit can use generative AI to customize support measures based on the current health state. This allows for more appropriate support by customizing support measures based on the current health state.

[0175] The generation unit can update the educational content by incorporating the latest research results. For example, the educational content is updated based on the latest research results. The generation unit can also improve the content of the educational content by incorporating the latest research results. Furthermore, the generation unit can update the educational content by incorporating the latest research results using the generative AI. This improves the accuracy of the educational content by incorporating the latest research results.

[0176] The provision unit can select the optimal provision method by referring to past provision data. For example, the optimal provision method is selected based on past provision data. The provision unit can also analyze past provision data and improve the provision method. Furthermore, the provision unit can use the generation AI to select the optimal provision method by referring to past provision data. In this way, the optimal provision method can be selected by referring to past provision data.

[0177] The processing flow of the second embodiment will be briefly explained below.

[0178] Step 1: The collection department collects past mission data and current technological status. For example, they collect data on the success rate of past missions, the technologies used, and environmental conditions, as well as the current technological status of the latest space exploration, communication, and life support technologies. Step 2: The proposal unit analyzes the data collected by the collection unit and proposes an efficient mission plan. For example, it proposes the optimal exploration route and necessary equipment based on past exploration data and the current technology level, and uses generation AI to make proposals for improving the efficiency of the mission. Step 3: The monitoring unit monitors the astronauts' health and work status in real time, including vital data such as heart rate, blood pressure, and oxygen saturation, as well as work status such as progress, type of work, and working hours. Step 4: The support department provides necessary support based on the data obtained by the monitoring department, such as advice on appropriate rest times and nutritional supplementation, as well as medical and psychological support to maintain health. Step 5: The generator generates educational content related to space, such as the history of space, the latest research results, and space exploration technology, and uses AI to generate educational content that is easy for the general public to understand. Step 6: The providing unit provides the educational content generated by the generating unit to the public, for example, through a website, an application, or as a printed material. Step 7: The analysis unit analyzes large amounts of space data, such as astronomical observation data and the results of space exploration, and uses generative AI to analyze the data. Step 8: The summarization section summarizes and provides the important information obtained by the analysis section. For example, it extracts and provides information that is useful to researchers and the general public, and uses generative AI to summarize the data.

[0179] The specific processing unit 290 transmits the result of the specific processing to the smart device 14. In the smart device 14, the control unit 46A causes the output device 40 to output the result of the specific processing. The microphone 38B acquires audio indicating a user input regarding the result of the specific processing. The control unit 46A transmits audio data indicating the user input acquired by the microphone 38B to the data processing device 12. In the data processing device 12, the specific processing unit 290 acquires the audio data.

[0180] The data generation model 58 is a so-called generative AI (Artificial Intelligence). An example of the data generation model 58 is ChatGPT (registered trademark) (Internet search engine).<URL: https: / / openai.com / blog / chatgpt> Examples of generative AIs include the data generation model 58, such as a neural network model (e.g., a neural network model), and a neural network model (e.g., a neural network model). The data generation model 58 is obtained by performing deep learning on a neural network. A prompt including an instruction is input to the data generation model 58, and inference data such as voice data indicating speech, text data indicating text, and image data indicating an image is also input to the data generation model 58. The data generation model 58 performs inference on the input inference data in accordance with the instruction indicated by the prompt and outputs the inference result in a data format such as voice data and text data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The specification processing unit 290 performs the above-mentioned specification processing using the data generation model 58. The data generation model 58 may be a fine-tuned model so as to output an inference result from a prompt that does not include an instruction. In this case, the data generation model 58 can output an inference result from a prompt that does not include an instruction. The data processing device 12 and the like include multiple types of data generation models 58, and the data generation model 58 includes AIs other than the generative AI. The AI ​​other than the generative AI may be, for example, linear regression, logistic regression, decision tree, random forest, support vector machine (SVM), k-means clustering, convolutional neural network (CNN), recurrent neural network (RNN), generative adversarial network (GAN), or naive Bayes, and can perform various processes, but is not limited to these examples. The AI ​​may also be an AI agent. When the processes of each of the above-mentioned parts are performed by AI, the processes may be performed in part or entirely by AI, but are not limited to these examples. The processes performed by AI, including the generative AI, may be replaced with rule-based processes.

[0181] Furthermore, the processing by the data processing system 10 described above is executed by the specific processing unit 290 of the data processing device 12 or the control unit 46A of the smart device 14, but may also be executed by the specific processing unit 290 of the data processing device 12 and the control unit 46A of the smart device 14. Furthermore, the specific processing unit 290 of the data processing device 12 acquires or collects information necessary for processing from the smart device 14 or an external device, and the smart device 14 acquires or collects information necessary for processing from the data processing device 12 or an external device.

[0182] The correspondence between each part and the device or control part is not limited to the above example, and various modifications are possible.

[0183] [Second embodiment] FIG. 3 shows an example of the configuration of a data processing system 210 according to the second embodiment.

[0184] 3, the data processing system 210 includes a data processing device 12 and smart glasses 214. An example of the data processing device 12 is a server.

[0185] The data processing device 12 includes a computer 22, a database 24, and a communication I / F 26. The computer 22 includes a processor 28, a RAM 30, and a storage 32. The processor 28, RAM 30, and storage 32 are connected to a bus 34. The database 24 and the communication I / F 26 are also connected to the bus 34. The communication I / F 26 is connected to a network 54. Examples of the network 54 include a WAN and / or a LAN.

[0186] The smart glasses 214 include a computer 36, a microphone 238, a speaker 240, a camera 42, and a communication I / F 44. The computer 36 includes a processor 46, a RAM 48, and a storage 50. The processor 46, the RAM 48, and the storage 50 are connected to a bus 52. The microphone 238, the speaker 240, and the camera 42 are also connected to the bus 52.

[0187] The microphone 238 receives instructions and the like from the user by receiving voice uttered by the user. The microphone 238 captures the voice uttered by the user, converts the captured voice into audio data, and outputs it to the processor 46. The speaker 240 outputs audio according to instructions from the processor 46.

[0188] Camera 42 is a small digital camera equipped with an optical system including a lens, aperture, and shutter, and an imaging element such as a CMOS (Complementary Metal-Oxide-Semiconductor) image sensor or a CCD (Charge Coupled Device) image sensor, and captures images of the user's surroundings (for example, an imaging range defined by an angle of view equivalent to the field of vision of a typical healthy person).

[0189] The communication I / F 44 is connected to a network 54. The communication I / Fs 44 and 26 are responsible for the exchange of various information between the processor 46 and the processor 28 via the network 54. The exchange of various information between the processor 46 and the processor 28 using the communication I / Fs 44 and 26 is carried out in a secure state.

[0190] Fig. 4 shows an example of the main functions of the data processing device 12 and the smart glasses 214. As shown in Fig. 4, in the data processing device 12, a specific process is performed by the processor 28. A specific process program 56 is stored in the storage 32.

[0191] The processor 28 reads the specific processing program 56 from the storage 32 and executes the read specific processing program 56 on the RAM 30. The specific processing is realized by the processor 28 operating as a specific processing unit 290 in accordance with the specific processing program 56 executed on the RAM 30.

[0192] The storage 32 stores a data generation model 58 and an emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290. The identification processing unit 290 can estimate a user's emotion using the emotion identification model 59 and perform identification processing using the user's emotion. The emotion estimation function (emotion identification function) using the emotion identification model 59 performs various estimations and predictions regarding the user's emotion, including estimation and prediction of the user's emotion, but is not limited to these examples. Furthermore, the estimation and prediction of emotion also includes, for example, emotion analysis.

[0193] In the smart glasses 214, the specific processing is performed by the processor 46. A specific processing program 60 is stored in the storage 50. The processor 46 reads the specific processing program 60 from the storage 50 and executes the read specific processing program 60 on the RAM 48. The specific processing is realized by the processor 46 operating as the control unit 46A in accordance with the specific processing program 60 executed on the RAM 48. The smart glasses 214 also have a data generation model and an emotion identification model similar to the data generation model 58 and the emotion identification model 59, and can perform processing similar to that of the specific processing unit 290 using these models.

[0194] Note that a device other than the data processing device 12 may have the data generation model 58. For example, a server device may have the data generation model 58. In this case, the data processing device 12 communicates with the server device having the data generation model 58 to obtain a processing result (such as a prediction result) using the data generation model 58. Furthermore, the data processing device 12 may be a server device, or may be a terminal device (for example, a mobile phone, a robot, a home appliance, etc.) owned by a user.

[0195] The specific processing unit 290 transmits the result of the specific processing to the smart glasses 214. In the smart glasses 214, the control unit 46A causes the speaker 240 to output the result of the specific processing. The microphone 238 acquires audio indicating a user input regarding the result of the specific processing. The control unit 46A transmits audio data indicating the user input acquired by the microphone 238 to the data processing device 12. In the data processing device 12, the specific processing unit 290 acquires the audio data.

[0196] The data generation model 58 is a so-called generative AI. An example of the data generation model 58 is a generative AI such as ChatGPT. The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 receives a prompt containing an instruction, as well as inference data such as voice data representing speech, text data representing text, and image data representing an image. The data generation model 58 performs inference on the input inference data in accordance with the instruction indicated by the prompt and outputs the inference result in a data format such as voice data and text data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The identification processing unit 290 performs the above-mentioned identification processing using the data generation model 58. The data generation model 58 may be a fine-tuned model so as to output an inference result from a prompt that does not include an instruction. In this case, the data generation model 58 can output an inference result from a prompt that does not include an instruction. The data processing device 12 and the like include multiple types of data generation models 58, and the data generation model 58 includes AI other than the generative AI. The AI ​​other than the generative AI may be, for example, linear regression, logistic regression, decision tree, random forest, support vector machine (SVM), k-means clustering, convolutional neural network (CNN), recurrent neural network (RNN), generative adversarial network (GAN), or naive Bayes, and can perform various processes, but is not limited to these examples. The AI ​​may also be an AI agent. When the processes of each of the above-mentioned parts are performed by AI, the processes may be performed in part or entirely by AI, but are not limited to these examples. The processes performed by AI, including the generative AI, may be replaced with rule-based processes.

[0197] The data processing system 210 according to the second embodiment performs the same processing as the data processing system 10 according to the first embodiment. The processing by the data processing system 210 is executed by the specific processing unit 290 of the data processing device 12 or the control unit 46A of the smart glasses 214, but may also be executed by the specific processing unit 290 of the data processing device 12 and the control unit 46A of the smart glasses 214. Furthermore, the specific processing unit 290 of the data processing device 12 acquires or collects information required for processing from the smart glasses 214 or an external device, etc., and the smart glasses 214 acquires or collects information required for processing from the data processing device 12 or an external device, etc.

[0198] The correspondence between each part and the device or control part is not limited to the above example, and various modifications are possible.

[0199] [Third embodiment] FIG. 5 shows an example of the configuration of a data processing system 310 according to the third embodiment.

[0200] 5, the data processing system 310 includes the data processing device 12 and a headset terminal 314. An example of the data processing device 12 is a server.

[0201] The data processing device 12 includes a computer 22, a database 24, and a communication I / F 26. The computer 22 includes a processor 28, a RAM 30, and a storage 32. The processor 28, RAM 30, and storage 32 are connected to a bus 34. The database 24 and the communication I / F 26 are also connected to the bus 34. The communication I / F 26 is connected to a network 54. Examples of the network 54 include a WAN and / or a LAN.

[0202] The headset type terminal 314 includes a computer 36, a microphone 238, a speaker 240, a camera 42, a communication I / F 44, and a display 343. The computer 36 includes a processor 46, a RAM 48, and a storage 50. The processor 46, the RAM 48, and the storage 50 are connected to a bus 52. The microphone 238, the speaker 240, the camera 42, and the display 343 are also connected to the bus 52.

[0203] The microphone 238 receives instructions and the like from the user by receiving voice uttered by the user. The microphone 238 captures the voice uttered by the user, converts the captured voice into audio data, and outputs it to the processor 46. The speaker 240 outputs audio according to instructions from the processor 46.

[0204] Camera 42 is a small digital camera equipped with an optical system including a lens, aperture, and shutter, and an imaging element such as a CMOS (Complementary Metal-Oxide-Semiconductor) image sensor or a CCD (Charge Coupled Device) image sensor, and captures images of the user's surroundings (for example, an imaging range defined by an angle of view equivalent to the field of vision of a typical healthy person).

[0205] The communication I / F 44 is connected to a network 54. The communication I / Fs 44 and 26 are responsible for the exchange of various information between the processor 46 and the processor 28 via the network 54. The exchange of various information between the processor 46 and the processor 28 using the communication I / Fs 44 and 26 is carried out in a secure state.

[0206] Fig. 6 shows an example of the main functions of the data processing device 12 and the headset terminal 314. As shown in Fig. 6, in the data processing device 12, a specific process is performed by the processor 28. A specific process program 56 is stored in the storage 32.

[0207] The processor 28 reads the specific processing program 56 from the storage 32 and executes the read specific processing program 56 on the RAM 30. The specific processing is realized by the processor 28 operating as a specific processing unit 290 in accordance with the specific processing program 56 executed on the RAM 30.

[0208] The storage 32 stores a data generation model 58 and an emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290. The identification processing unit 290 can estimate a user's emotion using the emotion identification model 59 and perform identification processing using the user's emotion. The emotion estimation function (emotion identification function) using the emotion identification model 59 performs various estimations and predictions regarding the user's emotion, including estimation and prediction of the user's emotion, but is not limited to these examples. Furthermore, the estimation and prediction of emotion also includes, for example, emotion analysis.

[0209] In the headset type terminal 314, the identification process is performed by the processor 46. A identification program 60 is stored in the storage 50. The processor 46 reads the identification program 60 from the storage 50 and executes the read identification program 60 on the RAM 48. The identification process is realized by the processor 46 operating as a control unit 46A in accordance with the identification program 60 executed on the RAM 48. Note that the headset type terminal 314 has a data generation model and an emotion identification model similar to the data generation model 58 and the emotion identification model 59, and can also perform processing similar to that of the identification processing unit 290 using these models.

[0210] Note that a device other than the data processing device 12 may have the data generation model 58. For example, a server device may have the data generation model 58. In this case, the data processing device 12 communicates with the server device having the data generation model 58 to obtain a processing result (such as a prediction result) using the data generation model 58. Furthermore, the data processing device 12 may be a server device, or may be a terminal device (for example, a mobile phone, a robot, a home appliance, etc.) owned by a user.

[0211] The specific processing unit 290 transmits the result of the specific processing to the headset type terminal 314. In the headset type terminal 314, the control unit 46A causes the speaker 240 and the display 343 to output the result of the specific processing. The microphone 238 acquires audio indicating a user input regarding the result of the specific processing. The control unit 46A transmits audio data indicating the user input acquired by the microphone 238 to the data processing device 12. In the data processing device 12, the specific processing unit 290 acquires the audio data.

[0212] The data generation model 58 is a so-called generative AI. An example of the data generation model 58 is a generative AI such as ChatGPT. The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 receives a prompt containing an instruction, as well as inference data such as voice data representing speech, text data representing text, and image data representing an image. The data generation model 58 performs inference on the input inference data in accordance with the instruction indicated by the prompt and outputs the inference result in a data format such as voice data and text data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The identification processing unit 290 performs the above-mentioned identification processing using the data generation model 58. The data generation model 58 may be a fine-tuned model so as to output an inference result from a prompt that does not include an instruction. In this case, the data generation model 58 can output an inference result from a prompt that does not include an instruction. The data processing device 12 and the like include multiple types of data generation models 58, and the data generation model 58 includes AI other than the generative AI. The AI ​​other than the generative AI may be, for example, linear regression, logistic regression, decision tree, random forest, support vector machine (SVM), k-means clustering, convolutional neural network (CNN), recurrent neural network (RNN), generative adversarial network (GAN), or naive Bayes, and can perform various processes, but is not limited to these examples. The AI ​​may also be an AI agent. When the processes of each of the above-mentioned parts are performed by AI, the processes may be performed in part or entirely by AI, but are not limited to these examples. The processes performed by AI, including the generative AI, may be replaced with rule-based processes.

[0213] The data processing system 310 according to the third embodiment performs the same processing as the data processing system 10 according to the first embodiment. The processing by the data processing system 310 is executed by the specific processing unit 290 of the data processing device 12 or the control unit 46A of the headset type terminal 314, but may also be executed by the specific processing unit 290 of the data processing device 12 and the control unit 46A of the headset type terminal 314. Furthermore, the specific processing unit 290 of the data processing device 12 acquires or collects information required for processing from the headset type terminal 314 or an external device, etc., and the headset type terminal 314 acquires or collects information required for processing from the data processing device 12 or an external device, etc.

[0214] The correspondence between each part and the device or control part is not limited to the above example, and various modifications are possible.

[0215] [Fourth embodiment] FIG. 7 shows an example of the configuration of a data processing system 410 according to the fourth embodiment.

[0216] 7, a data processing system 410 includes a data processing device 12 and a robot 414. An example of the data processing device 12 is a server.

[0217] The data processing device 12 includes a computer 22, a database 24, and a communication I / F 26. The computer 22 includes a processor 28, a RAM 30, and a storage 32. The processor 28, RAM 30, and storage 32 are connected to a bus 34. The database 24 and the communication I / F 26 are also connected to the bus 34. The communication I / F 26 is connected to a network 54. Examples of the network 54 include a WAN and / or a LAN.

[0218] The robot 414 includes a computer 36, a microphone 238, a speaker 240, a camera 42, a communication I / F 44, and a control target 443. The computer 36 includes a processor 46, a RAM 48, and a storage 50. The processor 46, the RAM 48, and the storage 50 are connected to a bus 52. The microphone 238, the speaker 240, the camera 42, and the control target 443 are also connected to the bus 52.

[0219] The microphone 238 receives instructions and the like from the user by receiving voice uttered by the user. The microphone 238 captures the voice uttered by the user, converts the captured voice into audio data, and outputs it to the processor 46. The speaker 240 outputs audio according to instructions from the processor 46.

[0220] Camera 42 is a small digital camera equipped with an optical system including a lens, aperture, and shutter, and an imaging element such as a CMOS image sensor or a CCD image sensor, and captures images of the user's surroundings (for example, an imaging range defined by an angle of view equivalent to the field of vision of a typical healthy person).

[0221] The communication I / F 44 is connected to a network 54. The communication I / Fs 44 and 26 are responsible for the exchange of various information between the processor 46 and the processor 28 via the network 54. The exchange of various information between the processor 46 and the processor 28 using the communication I / Fs 44 and 26 is carried out in a secure state.

[0222] The control object 443 includes a display device, LEDs in the eyes, and motors that drive the arms, hands, and feet. The posture and gestures of the robot 414 are controlled by controlling the motors of the arms, hands, and feet. Some of the emotions of the robot 414 can be expressed by controlling these motors. In addition, the facial expressions of the robot 414 can also be expressed by controlling the light emission state of the LEDs in the eyes of the robot 414.

[0223] Fig. 8 shows an example of the main functions of the data processing device 12 and the robot 414. As shown in Fig. 8, in the data processing device 12, a specific process is performed by the processor 28. A specific process program 56 is stored in the storage 32.

[0224] The processor 28 reads the specific processing program 56 from the storage 32 and executes the read specific processing program 56 on the RAM 30. The specific processing is realized by the processor 28 operating as a specific processing unit 290 in accordance with the specific processing program 56 executed on the RAM 30.

[0225] The storage 32 stores a data generation model 58 and an emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290. The identification processing unit 290 can estimate a user's emotion using the emotion identification model 59 and perform identification processing using the user's emotion. The emotion estimation function (emotion identification function) using the emotion identification model 59 performs various estimations and predictions regarding the user's emotion, including estimation and prediction of the user's emotion, but is not limited to these examples. Furthermore, the estimation and prediction of emotion also includes, for example, emotion analysis.

[0226] In the robot 414, the processor 46 performs the identification process. The storage 50 stores the identification program 60. The processor 46 reads the identification program 60 from the storage 50 and executes the read identification program 60 on the RAM 48. The identification process is realized by the processor 46 operating as the control unit 46A in accordance with the identification program 60 executed on the RAM 48. The robot 414 also has a data generation model and an emotion identification model similar to the data generation model 58 and the emotion identification model 59, and can perform the same process as the identification processing unit 290 using these models.

[0227] Note that a device other than the data processing device 12 may have the data generation model 58. For example, a server device may have the data generation model 58. In this case, the data processing device 12 communicates with the server device having the data generation model 58 to obtain a processing result (such as a prediction result) using the data generation model 58. Furthermore, the data processing device 12 may be a server device, or may be a terminal device (for example, a mobile phone, a robot, a home appliance, etc.) owned by a user.

[0228] The specific processing unit 290 transmits the result of the specific processing to the robot 414. In the robot 414, the control unit 46A causes the speaker 240 and the control target 443 to output the result of the specific processing. The microphone 238 acquires voice indicating a user input regarding the result of the specific processing. The control unit 46A transmits voice data indicating the user input acquired by the microphone 238 to the data processing device 12. In the data processing device 12, the specific processing unit 290 acquires the voice data.

[0229] The data generation model 58 is a so-called generative AI. An example of the data generation model 58 is a generative AI such as ChatGPT. The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 receives a prompt containing an instruction, as well as inference data such as voice data representing speech, text data representing text, and image data representing an image. The data generation model 58 performs inference on the input inference data in accordance with the instruction indicated by the prompt and outputs the inference result in a data format such as voice data and text data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The identification processing unit 290 performs the above-mentioned identification processing using the data generation model 58. The data generation model 58 may be a fine-tuned model so as to output an inference result from a prompt that does not include an instruction. In this case, the data generation model 58 can output an inference result from a prompt that does not include an instruction. The data processing device 12 and the like include multiple types of data generation models 58, and the data generation model 58 includes AI other than the generative AI. The AI ​​other than the generative AI may be, for example, linear regression, logistic regression, decision tree, random forest, support vector machine (SVM), k-means clustering, convolutional neural network (CNN), recurrent neural network (RNN), generative adversarial network (GAN), or naive Bayes, and can perform various processes, but is not limited to these examples. The AI ​​may also be an AI agent. When the processes of each of the above-mentioned parts are performed by AI, the processes may be performed in part or entirely by AI, but are not limited to these examples. The processes performed by AI, including the generative AI, may be replaced with rule-based processes.

[0230] The data processing system 410 according to the fourth embodiment performs the same processing as the data processing system 10 according to the first embodiment. The processing by the data processing system 410 is executed by the specific processing unit 290 of the data processing device 12 or the control unit 46A of the robot 414, but may also be executed by the specific processing unit 290 of the data processing device 12 and the control unit 46A of the robot 414. Furthermore, the specific processing unit 290 of the data processing device 12 acquires or collects information required for processing from the robot 414 or an external device, etc., and the robot 414 acquires or collects information required for processing from the data processing device 12 or an external device, etc.

[0231] The correspondence between each part and the device or control part is not limited to the above example, and various modifications are possible.

[0232] The emotion identification model 59 as an emotion engine may determine the user's emotion according to a specific mapping. Specifically, the emotion identification model 59 may determine the user's emotion according to an emotion map (see FIG. 9), which is a specific mapping. Similarly, the emotion identification model 59 may determine the robot's emotion, and the identification processing unit 290 may perform identification processing using the robot's emotion.

[0233] FIG. 9 illustrates an emotion map 400 on which multiple emotions are mapped. In the emotion map 400, emotions are arranged in concentric circles radiating from the center. Emotions closer to the center of the concentric circles are more primitive. Emotions representing states and behaviors arising from a state of mind are arranged on the outer edges of the concentric circles. The concept of emotion encompasses both emotions and mental states. Emotions generally generated from reactions occurring in the brain are arranged on the left side of the concentric circles. Emotions generally induced by situational judgment are arranged on the right side of the concentric circles. Emotions generally generated from reactions occurring in the brain and induced by situational judgment are arranged on the upper and lower sides of the concentric circles. Furthermore, the emotion of "pleasure" is arranged on the upper side of the concentric circles, and the emotion of "discomfort" is arranged on the lower side. In this way, in the emotion map 400, multiple emotions are mapped based on the structure by which emotions are generated, and emotions that tend to occur simultaneously are mapped close to each other.

[0234] These emotions are distributed in the 3 o'clock direction on emotion map 400, and typically fluctuate between relief and anxiety. In the right half of emotion map 400, situational awareness dominates over internal sensations, resulting in a sense of calm.

[0235] The inside of emotion map 400 represents what is going on in the mind, and the outside of emotion map 400 represents behavior, so the further you go outside emotion map 400, the more visible the emotions become (the more they are expressed in behavior).

[0236] Human emotions are based on various balances, such as posture and blood sugar levels. When these balances deviate from the ideal, a state of discomfort is expressed, and when they approach the ideal, a state of pleasure is expressed. Emotions can also be created for robots, cars, and motorcycles, based on various balances, such as posture and remaining battery life. When these balances deviate from the ideal, a state of discomfort is expressed, and when they approach the ideal, a state of pleasure is expressed. An emotion map can be generated, for example, based on Dr. Mitsuyoshi's emotion map (Research on speech emotion recognition and brain physiological signal analysis systems for emotions, Tokushima University, doctoral dissertation: https: / / ci.nii.ac.jp / naid / 500000375379). The left half of the emotion map lists emotions belonging to the area called "reaction," where sensation is dominant. The right half of the emotion map lists emotions belonging to the area called "situation," where situational awareness is dominant.

[0237] The emotion map defines two emotions that promote learning. One is a negative emotion on the situation side, around the middle of "repentance" or "reflection." In other words, this occurs when the robot experiences negative emotions such as "I never want to feel this way again" or "I don't want to be scolded again." The other is a positive emotion on the response side, around "desire." In other words, this occurs when the robot experiences positive feelings such as "I want more" or "I want to know more."

[0238] The emotion identification model 59 inputs user input into a pre-trained neural network, obtains emotion values ​​indicating each emotion shown in the emotion map 400, and determines the user's emotion. This neural network is pre-trained based on multiple pieces of training data that are combinations of user input and emotion values ​​indicating each emotion shown in the emotion map 400. Furthermore, this neural network is trained so that emotions that are located close to each other have similar values, as in the emotion map 900 shown in FIG. 10. FIG. 10 shows an example in which multiple emotions, "relieved," "calm," and "reassuring," have similar emotion values.

[0239] In the above embodiment, an example was given in which a specific process is performed by one computer 22, but the technology disclosed herein is not limited to this, and distributed processing of the specific process may be performed by multiple computers including computer 22.

[0240] In the above embodiment, an example in which the specific processing program 56 is stored in the storage 32 has been described, but the technology of the present disclosure is not limited to this. For example, the specific processing program 56 may be stored in a portable, computer-readable, non-transitory storage medium such as a USB (Universal Serial Bus) memory. The specific processing program 56 stored in the non-transitory storage medium is installed in the computer 22 of the data processing device 12. The processor 28 executes the specific processing in accordance with the specific processing program 56.

[0241] Alternatively, the specific processing program 56 may be stored in a storage device such as a server connected to the data processing device 12 via the network 54, and the specific processing program 56 may be downloaded and installed on the computer 22 in response to a request from the data processing device 12.

[0242] It is not necessary to store all of the specific processing program 56 in a storage device such as a server connected to the data processing device 12 via the network 54, or to store all of the specific processing program 56 in the storage 32; only a portion of the specific processing program 56 may be stored.

[0243] The hardware resource for executing a specific process can be any of the following types of processors: A processor, for example, is a CPU, which is a general-purpose processor that functions as a hardware resource for executing a specific process by executing software, i.e., a program. A processor also includes a dedicated electrical circuit, such as an FPGA (Field-Programmable Gate Array), a PLD (Programmable Logic Device), or an ASIC (Application Specific Integrated Circuit), which is a processor with a circuit configuration designed specifically for executing a specific process. Each processor has built-in or connected memory, and each processor uses the memory to execute the specific process.

[0244] The hardware resource that executes the specific process may be configured with one of these various processors, or may be configured with a combination of two or more processors of the same or different types (for example, a combination of multiple FPGAs, or a combination of a CPU and an FPGA). Also, the hardware resource that executes the specific process may be a single processor.

[0245] As an example of a system configured with a single processor, first, one processor is configured by combining one or more CPUs and software, and this processor functions as a hardware resource that executes a specific process. Second, there is a system that uses a processor that realizes the functions of an entire system including multiple hardware resources that execute a specific process on a single IC chip, as typified by SoC (System-on-a-chip). In this way, a specific process is realized using one or more of the above-mentioned various processors as hardware resources.

[0246] Furthermore, the hardware structure of these various processors can be, more specifically, an electric circuit that combines circuit elements such as semiconductor devices. The specific processing described above is merely an example. Therefore, it goes without saying that unnecessary steps may be deleted, new steps may be added, or the processing order may be rearranged, without departing from the spirit of the invention.

[0247] In the above example, the first to fourth embodiments have been described separately, but some or all of these embodiments may be combined. The smart device 14, smart glasses 214, headset terminal 314, and robot 414 are merely examples, and they may be combined, or other devices may be used. In the above example, the first and second embodiments have been described separately, but they may be combined.

[0248] The above-described description and illustrations are a detailed explanation of the parts related to the technology of the present disclosure and are merely an example of the technology of the present disclosure. For example, the above description of the configuration, functions, actions, and effects is an explanation of an example of the configuration, functions, actions, and effects of the parts related to the technology of the present disclosure. Therefore, it goes without saying that unnecessary parts may be deleted, new elements may be added, or replacements may be made to the above-described description and illustrations within the scope of the gist of the technology of the present disclosure. Furthermore, in order to avoid confusion and to facilitate understanding of the parts related to the technology of the present disclosure, the above-described description and illustrations omit explanations of common technical knowledge that do not require particular explanation to enable the implementation of the technology of the present disclosure.

[0249] All publications, patent applications, and technical standards mentioned in this specification are herein incorporated by reference to the same extent as if each individual publication, patent application, or technical standard was specifically and individually indicated to be incorporated by reference.

[0250] [Explanation of symbols]

[0251] 10, 210, 310, 410 Data Processing Systems 12 Data Processing Device 14 Smart Devices 214 Smart Glasses 314 Headset-type terminal 414 Robot

Claims

1. A collection department that collects past mission data and current technical status; a proposal unit that analyzes the data collected by the collection unit and proposes an efficient mission plan; The monitoring department monitors the astronauts' health and work status, and a support unit that provides necessary support based on the data obtained by the monitoring unit; a generation unit that generates educational content related to space; a providing unit that provides the content generated by the generating unit; The analysis department analyzes large amounts of space data, a summarizing unit that summarizes and provides important information obtained by the analyzing unit. A system characterized by:

2. The proposal unit Propose efficient exploration routes and necessary equipment based on past exploration data and current technology levels 2. The system of claim 1.

3. The monitoring unit Real-time monitoring of astronauts' vital data and work status 2. The system of claim 1.

4. The support portion is Providing advice on appropriate rest times and nutrition 2. The system of claim 1.

5. The generation unit Generate educational content based on space knowledge and the latest research results 2. The system of claim 1.

6. The providing unit Providing generated educational content to the general public 2. The system of claim 1.

7. The analysis unit Analyzing astronomical observation data and space exploration results 2. The system of claim 1.

8. The summary section Extract and provide useful information for researchers and the general public 2. The system of claim 1.

Images